{"as_of":"2026-08-08T21:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f4ee9912287f21c44233a9843cff6ec17eb0b39cd5b1ced6086b5a576acb9f57","coverage":[{"denominator":300,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:29:54.304076Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.15023/citation-record","integrity":"/paper/2505.15023/integrity","json":"/paper/2505.15023/citation-record.json","paper":"/paper/2505.15023"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:46.770138Z","title":"Asuncion, Arthur U","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:46.770138Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:05595af4daa639f1756275c07f7e9eb3532bb667b737bc3e0063315954bd95c3","observation_id":"3e722ac8-7b3c-4666-a6ac-e529c44bb74e","resolution":{"observed_at":"2026-08-07T15:29:46.770138Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:46.846108Z","title":"Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:46.846108Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:6da87cdb4ec2f550568923e43727c426b0a751db9173b8c84d6465eba1ecc756","observation_id":"bed7c5eb-2283-4b4d-ad82-850fa002ea07","resolution":{"observed_at":"2026-08-07T15:29:46.846108Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:46.948464Z","title":"Learning to represent programs with graphs","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:46.948464Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:86f27d3d484eaab8c3fb29ee0e1441fe0e2a96f44a78672dfb1678fa5e7a70b5","observation_id":"80f400f1-edc8-4c4c-b0cf-ca8beaf29658","resolution":{"observed_at":"2026-08-07T15:29:46.948464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:47.063915Z","title":"Information retrieval models for recovering traceability links between code and documentation","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:47.063915Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:73cdf428ae6161cd42ae4e50cef99e169973478a7a3d851f31bc1cd1b717d1c1","observation_id":"06bae4f2-6b61-4088-acee-090d726c7654","resolution":{"observed_at":"2026-08-07T15:29:47.063915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:47.164557Z","title":"Unified pre-training for program understanding and generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:47.164557Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:cd0f84acea54e3aed27420be7208099e09f8518833808d0852ff8e4e1a038710","observation_id":"23f59c43-c3cc-4a54-bb95-0569cb21824a","resolution":{"observed_at":"2026-08-07T15:29:47.164557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:47.268659Z","title":"Albrecht, Filippos Christianos, and Lukas Sch\\\"afer","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:47.268659Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:c05a97829c62595ac4ba9162af8b56d51f7db8f734b10c4a359094b0305e1f1a","observation_id":"c3ffbf13-8430-46c8-89e9-edcb86e1a8f7","resolution":{"observed_at":"2026-08-07T15:29:47.268659Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:47.389482Z","title":"A literature review of automatic traceability links recovery for software change impact analysis","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:47.389482Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:2b1f56916690a6f6bcf765480d65b11a024b3cd283d6e91b0fbe7149813225a3","observation_id":"dd0b6b09-fbd3-4ba3-9e19-fb5ef0cc44ba","resolution":{"observed_at":"2026-08-07T15:29:47.389482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:47.503416Z","title":"The adverse effects of code duplication in machine learning models of code","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:47.503416Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:f69c7e88b3b258e2df5e47a316eb066ca46bea715f6624a7ca0598c1aec15939","observation_id":"f88d7351-901c-4e77-9863-68350270a036","resolution":{"observed_at":"2026-08-07T15:29:47.503416Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:47.599058Z","title":"Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:47.599058Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:4057512d4d1718eb44cba071224862b3c4abc1315d495f8102451bd4b8fc490d","observation_id":"f25bd60b-5efa-4470-9703-4678a7d8a89c","resolution":{"observed_at":"2026-08-07T15:29:47.599058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:47.690525Z","title":"The claude 3 model family: Opus, sonnet, haiku, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:47.690525Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:f7c33aecdaf7a10ab56dbf9a051aa4ee0625955df53299101eee73b3143dfd89","observation_id":"2213d293-2110-4db1-9dea-d7aa5a4e95e0","resolution":{"observed_at":"2026-08-07T15:29:47.690525Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:47.757443Z","title":"Program synthesis with large language models, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:47.757443Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:103b4da79870ef42c59fde630de803e468b5b93de78fafa8beac6e4fb0db5c42","observation_id":"155526d2-dbb1-43d9-b323-507c94c9b8e0","resolution":{"observed_at":"2026-08-07T15:29:47.757443Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:47.819706Z","title":"Bishop and Hugh Bishop","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:47.819706Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:d0464afdd7c1d70e3ea3efda67a67f478d277f096cd551dce1733103b2096d53","observation_id":"22fe03d1-58d2-45c7-9b80-0172bd44accd","resolution":{"observed_at":"2026-08-07T15:29:47.819706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:47.879750Z","title":"Gpt-neox-20b: An open-source autoregressive language model, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:47.879750Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:f2160673640c986a1a658f9a99a660362eb10ffe458145e516a5402a1c102f7e","observation_id":"4d66507c-e0b6-4540-85b9-3a8b22b881ca","resolution":{"observed_at":"2026-08-07T15:29:47.879750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:47.956207Z","title":"Maximum a posteriori estimators as a limit of Bayes estimators , 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:47.956207Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:15f35223c6a103a2560efe0b20c578df237354cc02189bf04ec19f5b75f0fc0d","observation_id":"4ffabdb6-6f90-45a3-bd04-496ea5dca13f","resolution":{"observed_at":"2026-08-07T15:29:47.956207Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.020400Z","title":"A neural probabilistic language model","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.020400Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:c6aec1fccb3c263eecb67073d5bb2d90f724570f65ecb784c1bdd2d7eefbf7a2","observation_id":"c8a172ea-ed64-4d66-8e8f-8f3b841dcef1","resolution":{"observed_at":"2026-08-07T15:29:48.020400Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.083556Z","title":"Artificial Life","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.083556Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:1614fd2ab33542f53f4e5aa39e727ff1a75b800f48380363e8a2f9942bd8f120","observation_id":"35b68ac2-1817-4d97-a624-15e61fb13f0c","resolution":{"observed_at":"2026-08-07T15:29:48.083556Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.165212Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.165212Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:0f8701ea4fec6e2da9de927473cecc105405a05b26b4f870f53daae6f6d4abeb","observation_id":"c5a1762a-e9bd-4780-9eaf-38a34a9b9615","resolution":{"observed_at":"2026-08-07T15:29:48.165212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.232306Z","title":"Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.232306Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:51ff74f0e191289b7cd4bca5a4262e280c03c1995f9b309a79a16083606573fa","observation_id":"7a0ec2f2-2880-4da7-9037-eea865922ac5","resolution":{"observed_at":"2026-08-07T15:29:48.232306Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.316270Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.316270Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:96aae19d0de00df977a4f3cb3b3a00e530ebc51121669836f4aeea29ef1b3c88","observation_id":"3eaabff7-bfc8-4ad5-8e4e-2106ec93d5c1","resolution":{"observed_at":"2026-08-07T15:29:48.316270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.391048Z","title":"Natural Language Processing with Python","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.391048Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:10166f001ad9c7fa30d93e037c8c837ee871b79630e4a6095924ee37633d9b15","observation_id":"193a32d8-1ad8-43f2-a22c-ed572ca52985","resolution":{"observed_at":"2026-08-07T15:29:48.391048Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.437921Z","title":"NLTK : The natural language toolkit","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.437921Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:5f48affcc71e7066116d04374a978107e28f89e2adb804cc52c7f55d9b0bda88","observation_id":"5e74113e-10c1-4843-a28c-89a869a41f39","resolution":{"observed_at":"2026-08-07T15:29:48.437921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.468409Z","title":"API design for machine learning software: experiences from the scikit-learn project","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.468409Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:54da23d2cbd7861b477ea9667854d4ab65edbce385175474ba8d0c1f8dd285b6","observation_id":"93f2c443-9caa-4d33-bed2-efd7f9a25a54","resolution":{"observed_at":"2026-08-07T15:29:48.468409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.511404Z","title":"A model-driven architecture approach to accelerate software code generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.511404Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:056441dc33f5d3b6399ade05def1d4c104ef5e1507e1bdf617c34aa178740f31","observation_id":"69c5b8a7-735b-49e1-a6cf-bf28d522166b","resolution":{"observed_at":"2026-08-07T15:29:48.511404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.550484Z","title":"On identifiability in transformers, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.550484Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:67c9d866a9bb8ca4b20624c2a7bfa7faa596e878b9dea9cb05dfdb1f82dd015b","observation_id":"939a3f70-ada1-4069-87da-8e23860999cc","resolution":{"observed_at":"2026-08-07T15:29:48.550484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.593806Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.593806Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:f4341ce151fcf2320c002e99aaa489ee45df52041c168fdef8ebf6b2e1ea9b8f","observation_id":"48ddcedc-72cf-4504-877e-8b9dddaeb37e","resolution":{"observed_at":"2026-08-07T15:29:48.593806Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.634671Z","title":"Biggerstaff, Bharat G","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.634671Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:5535842732244e4b5f4ba7a06683adf082991f3c570bd2d62c68d35fe88dc1d0","observation_id":"ebad0ad8-1ab8-4c2d-8c57-ed133545829c","resolution":{"observed_at":"2026-08-07T15:29:48.634671Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.683167Z","title":"tree-sitter/tree-sitter: v0.25.3, March 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.683167Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:e10ea3541e91da8033a7990a3d77f8a388ee502acfe76bba01e5d3c4964624d7","observation_id":"9d0c35a2-660a-4f8c-9d6b-d207985244f2","resolution":{"observed_at":"2026-08-07T15:29:48.683167Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.07764","last_updated":"2021-10-20T18:32:48Z","snapshot_observed_at":"2026-08-07T18:31:35.230552Z","submitted_at":"2020-02-18T17:53:55Z","title":"Sampling in Software Engineering Research: A Critical Review and Guidelines","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.07764","snapshot_observed_at":"2026-08-07T15:29:48.726361Z","title":"Sampling in Software Engineering Research : A Critical Review and Guidelines , October 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.726361Z"},"links":{"cited_paper":"/paper/2002.07764","citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:10a83573b8f757da38db504070f9e8b605aa2ce414a7f635c70e58ea14dde33d","observation_id":"c68894a5-8c6d-4776-9104-84b0aa853ad6","resolution":{"observed_at":"2026-08-07T15:29:48.726361Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.766253Z","title":null,"venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.766253Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:a50cdc7cee84404eaddc1b22f2a0f923f4bbc05cb6429d8f789900f7f46951af","observation_id":"78f52fdd-a08f-4c49-8045-02b0078aa3ea","resolution":{"observed_at":"2026-08-07T15:29:48.766253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.798978Z","title":"Ullman, Fernando Martinez-Plumed, Joshua B","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.798978Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:bea3e028ce92777efa1bfc274d3ba85de8a9c7bd43582996f78b09d0af4b3170","observation_id":"728d78b2-6134-4937-93d1-fc2c53c4ceed","resolution":{"observed_at":"2026-08-07T15:29:48.798978Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.865707Z","title":"Causality: The Place of the Causal Principle in Modern Science","venue":null,"work_id":null,"year":1959},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.865707Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:975712f5edf24e86336c3c90dad16491154a4d063ea1b5ca820c58a58dd44618","observation_id":"5bb044df-3ab9-4a0c-b5e8-c80785ed4a2a","resolution":{"observed_at":"2026-08-07T15:29:48.865707Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.925446Z","title":"Emergence and Convergence: Qualitative Novelty and the Unity of Knowledge","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.925446Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:3fc4be622adbb4963c87eb028d5fc7a9bd169c38d2c0ed7c85573cb3314b9b04","observation_id":"8a735b1c-07f9-4e39-8bd1-d471ae2c4698","resolution":{"observed_at":"2026-08-07T15:29:48.925446Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:48.987035Z","title":"Philosophy of Science: Volume 1 and 2","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:48.987035Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:dbdc03c4b8cd6d3d0bdbf2392d1c7d443979d89cafab46e09d58c623bf1ba65e","observation_id":"3f7483dc-78c9-481b-91e9-29c78bfb0713","resolution":{"observed_at":"2026-08-07T15:29:48.987035Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.069925Z","title":"The care and feeding of wild-caught mutants","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.069925Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:1f00b822300b9f3e25c626fcb55bddc65b7c950adf650b1ad6497c7c54a08666","observation_id":"b2f7228e-7178-4b0d-8d55-ef48c80abe05","resolution":{"observed_at":"2026-08-07T15:29:49.069925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.141019Z","title":"An empirical exploration of trust dynamics in llm supply chains, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.141019Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:30e35f3fdc69cd3dea9d04d804b36ad12f30556ae0512aebe9d1ac2d680b491c","observation_id":"1bf0d687-4483-44c4-babf-2cf703040b48","resolution":{"observed_at":"2026-08-07T15:29:49.141019Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.215736Z","title":"Nature’s Capacities and Their Measurement","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.215736Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:0a20da51e7c3942327bcf7b1ac91aba198527a8ef52d7c59fc48028957f601c7","observation_id":"39e8eb37-533a-459e-b8db-b3c44c4330ae","resolution":{"observed_at":"2026-08-07T15:29:49.215736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.292921Z","title":"The Dappled World: A Study of the Boundaries of Science","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.292921Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:d916707b625a5782796cdd964af318ab3b5487364468010ee5f4dff73b6a98a1","observation_id":"738a59d0-7f51-47f2-a544-3f9944736dcd","resolution":{"observed_at":"2026-08-07T15:29:49.292921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.400177Z","title":"Hunting Causes and Using Them: Approaches in Philosophy and Economics","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.400177Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:1927119526056e23c6b8df0d04df081ba044860d5dc6fdcd5bc5fa26e9a02137","observation_id":"1b8683a6-742c-4933-9571-5a5c33f63b8c","resolution":{"observed_at":"2026-08-07T15:29:49.400177Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.450990Z","title":"An empirical study on the usage of bert models for code completion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.450990Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:2aa5711aad6b4f2028162032fd39176118803eeae89ffb71777f15da7f922f6e","observation_id":"52c59e40-ce57-4ad0-98a1-952b61a19f27","resolution":{"observed_at":"2026-08-07T15:29:49.450990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.07115","last_updated":"2021-03-12T07:26:25Z","snapshot_observed_at":"2026-08-03T16:41:11.285404Z","submitted_at":"2021-03-12T07:26:25Z","title":"An Empirical Study on the Usage of BERT Models for Code Completion","version":1},"cited_work":{"arxiv_id":"2103.07115","doi":null,"metadata_source":"pith","pith_arxiv_id":"2103.07115","snapshot_observed_at":"2026-08-07T15:30:11.164493Z","title":"An Empirical Study on the Usage of BERT Models for Code Completion","venue":"cs.SE","work_id":"e96307f8-84ab-4383-b16d-ec68606adcdc","year":2021},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.502211Z"},"links":{"cited_paper":"/paper/2103.07115","citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:d2a0cd51475eeae283badb5c12c030eb53775332ccbbdf7ffd2efb51f2d4110f","observation_id":"17f7f866-7732-4a00-9012-dbbf02b1b9e3","resolution":{"observed_at":"2026-08-07T15:30:11.245738Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.546949Z","title":"An empirical study on the usage of transformer models for code completion","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.546949Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:e68abea1a926ce04cc09a582fb70724e63b111597bf00ef55d87c5e012aee730","observation_id":"4eca3282-80fc-478f-8e33-fe497e37dd76","resolution":{"observed_at":"2026-08-07T15:29:49.546949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.600323Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.600323Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:9ce987d10f8bd17f8d82384e84a259047c2d4d7491caa5f2863dd6ef75ad6bfe","observation_id":"79a64c81-4102-4109-8fd5-3e8354710ebe","resolution":{"observed_at":"2026-08-07T15:29:49.600323Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.631135Z","title":"Counterfactual explanations for models of code","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.631135Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:892d25f4a3b289ac46b98a8821fe11152796d48c454b9b96b1072ad61b8c52a9","observation_id":"8140c885-a985-4270-b17f-11be57b7d693","resolution":{"observed_at":"2026-08-07T15:29:49.631135Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.08227","last_updated":"2022-12-19T10:30:12Z","snapshot_observed_at":"2026-07-06T13:42:40.131502Z","submitted_at":"2022-08-17T11:16:52Z","title":"MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.08227","snapshot_observed_at":"2026-08-07T15:29:49.670235Z","title":"Feldman, Arjun Guha, Michael Greenberg, and Abhinav Jangda","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.670235Z"},"links":{"cited_paper":"/paper/2208.08227","citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:981ca9a4ae349bb36b5e4b0c644948f6f619d7afc6344a7e4184ab8bd9bdd676","observation_id":"b433847b-73ec-43e4-95e8-cdc00ff52f7f","resolution":{"observed_at":"2026-08-07T15:29:49.670235Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.712433Z","title":"Constructing Grounded Theory: A Practical Guide through Qualitative Analysis","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.712433Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:f63047029b85b16786f724e34233155b7f96cca21f311c9fc09be86d5f1e5131","observation_id":"7a513190-2f09-448e-ada5-79c3d5e1ea99","resolution":{"observed_at":"2026-08-07T15:29:49.712433Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.746463Z","title":"Chang, and Mark Christensen","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.746463Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:8fb98cb8b96ae7eafe9a7ee1e4a89b641b46e3a61df50351619488f7d4e3de25","observation_id":"5ba19121-3262-4e5e-8204-ccb4e904fc50","resolution":{"observed_at":"2026-08-07T15:29:49.746463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.789091Z","title":"A machine learning approach for tracing regulatory codes to product specific requirements","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.789091Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:d28e1778df35bab52b954de5ef563005bd8ef31e45f5e867e94ae320899c5f2e","observation_id":"5912d103-110e-481c-bde5-57756c7747db","resolution":{"observed_at":"2026-08-07T15:29:49.789091Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.819948Z","title":"Connor, A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.819948Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:881989f376e809649ec063eaa9606e25d0a83b4622dc2d2e71769ddb50bb6b32","observation_id":"66213aa2-cd41-451c-bf4f-2a636f9d80ce","resolution":{"observed_at":"2026-08-07T15:29:49.819948Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.846843Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.846843Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:87be8fdcdbca50f3f0a67961fa19355166ccb5c164368d65bc9ed39e02233b84","observation_id":"dcddaf45-f62c-4300-94da-3b3962b50a37","resolution":{"observed_at":"2026-08-07T15:29:49.846843Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.886071Z","title":"Software and Systems Traceability","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.886071Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:781dc716682567aa443d8d2aa01d373381b93015e636b6214efb664383720772","observation_id":"9e2245eb-9036-4ec6-97d4-99dae54408e6","resolution":{"observed_at":"2026-08-07T15:29:49.886071Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.934106Z","title":"Achieving lightweight trustworthy traceability","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.934106Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:c68d4d7761fc29bfdb5a046c5848628ad78682a9d1605f1590dab6da10aef3f6","observation_id":"b62b5c67-f5af-406e-83e2-3a88a77f4757","resolution":{"observed_at":"2026-08-07T15:29:49.934106Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.965814Z","title":"Sequencer: Sequence-to-sequence learning for end-to-end program repair","venue":null,"work_id":null,"year":1943},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.965814Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:1f013d2ff63a537928bffe5e1a01c274687963698cb8cccb85485496f6ef4465","observation_id":"d810da11-a2cf-4802-a635-c0036f117bad","resolution":{"observed_at":"2026-08-07T15:29:49.965814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:49.999264Z","title":"Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, and Alexey Svyatkovskiy","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:49.999264Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:b86205828599a4c0234d61a22f590690901079b5fea20988e53c398eddaeaab8","observation_id":"f0c52eee-0374-4a6d-af26-9c95ee1bee6e","resolution":{"observed_at":"2026-08-07T15:29:49.999264Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:50.068614Z","title":"Bigquery: Serverless, highly scalable, and cost-effective multi-cloud data warehouse, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:50.068614Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:8a91d79e671a98c57a9ce213f65908888b14a0ae8e34a60a67a4a7cc9fe31ecc","observation_id":"ee61284d-4dde-4461-a85d-54454fd06415","resolution":{"observed_at":"2026-08-07T15:29:50.068614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:50.153789Z","title":"Debugging tool for code generation neural language models, March 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:50.153789Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:fc81b9b48cb22facfd15779d86807b638b0d5eea52f8cbeff38b9bc6de8d42eb","observation_id":"d79db10f-68f7-4bd9-a8b8-a05cef64130d","resolution":{"observed_at":"2026-08-07T15:29:50.153789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:50.205749Z","title":"Cisco Systems","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:50.205749Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:78957c3006f709a292a790f65ef11597027f0bba1905d9da803091404fb731cf","observation_id":"2892dd3d-571e-4e5a-9c6c-1a75c6ec324f","resolution":{"observed_at":"2026-08-07T15:29:50.205749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:50.220913Z","title":"Improving smart contract security with contrastive learning-based vulnerability detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:50.220913Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:4fd13cbefa431cd140e4cd6236595396a32d9c12080f1160b697081047c132af","observation_id":"154ccf3a-2c1c-4dee-bb1d-7fde6fcd326a","resolution":{"observed_at":"2026-08-07T15:29:50.220913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.05667","last_updated":"2024-07-12T15:34:29Z","snapshot_observed_at":"2026-07-06T14:16:48.496031Z","submitted_at":"2022-11-10T16:04:28Z","title":"What Makes a Good Explanation?: A Harmonized View of Properties of Explanations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.05667","snapshot_observed_at":"2026-08-07T15:29:50.308670Z","title":"What Makes a Good Explanation ?: A Harmonized View of Properties of Explanations , December 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:50.308670Z"},"links":{"cited_paper":"/paper/2211.05667","citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:a7655fe185cee7792b5813aaf675c462b5d777d8186c00c0b265879b8425b850","observation_id":"9e2f10e5-26fc-4b6f-a3fc-5023498f6650","resolution":{"observed_at":"2026-08-07T15:29:50.308670Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:50.457607Z","title":"Snopy: Bridging sample denoising with causal graph learning for effective vulnerability detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:50.457607Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:4e6d85c2f5c435df553bb47a9cd2f5a1a4c84e8a258bcc2cd8a11a96229f58e2","observation_id":"965ddb7b-aace-4983-a94c-c3d6b85c2a43","resolution":{"observed_at":"2026-08-07T15:29:50.457607Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:50.518459Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:50.518459Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:f588fdac5642b4cc59487c6d669016c88b818285dad8db83c10be5a02366b769","observation_id":"20d8090e-6ca5-4605-94e2-5adf0900edc8","resolution":{"observed_at":"2026-08-07T15:29:50.518459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1259","last_updated":"2014-10-07T18:08:30Z","snapshot_observed_at":"2026-07-06T03:53:24.366023Z","submitted_at":"2014-09-03T21:03:41Z","title":"On the Properties of Neural Machine Translation: Encoder-Decoder Approaches","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1259","snapshot_observed_at":"2026-08-07T15:29:50.572445Z","title":"On the Properties of Neural Machine Translation : Encoder - Decoder Approaches , October 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:50.572445Z"},"links":{"cited_paper":"/paper/1409.1259","citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:273c230e8c15d7310133f8927c989dc38e917ddcf2b9998569e87a100473298f","observation_id":"fa67bcfd-ff6c-459b-a217-876eccc082b7","resolution":{"observed_at":"2026-08-07T15:29:50.572445Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:50.662212Z","title":"A survey on open source software trustworthiness","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:50.662212Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:940e5f82b428a5629a2ff5e8c7527684d8abf375618616e152c5f0f7474a089d","observation_id":"907423ae-a612-4de5-be04-0358fce9209a","resolution":{"observed_at":"2026-08-07T15:29:50.662212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08696","last_updated":"2022-03-10T02:28:59Z","snapshot_observed_at":"2026-08-05T12:39:09.493409Z","submitted_at":"2021-04-18T03:38:26Z","title":"Knowledge Neurons in Pretrained Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08696","snapshot_observed_at":"2026-08-07T15:29:50.727326Z","title":"Knowledge neurons in pretrained transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:50.727326Z"},"links":{"cited_paper":"/paper/2104.08696","citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:7eb51608b669281057cf1a591a87a6300f9d303879cd0854e3c3f62b0be5f7d8","observation_id":"471b2b56-b5ad-45cc-92e4-9259004b0f83","resolution":{"observed_at":"2026-08-07T15:29:50.727326Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:50.796028Z","title":"Enhancing software traceability by automatically expanding corpora with relevant documentation","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:50.796028Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:25673c84adeebe213356a9366fecd0306e8570bd20169574b99511ba5f6ab402","observation_id":"1a84db26-2bd4-4a86-9859-b7cff6158264","resolution":{"observed_at":"2026-08-07T15:29:50.796028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:50.867777Z","title":"Technique integration for requirements assessment","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:50.867777Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:af8fcd8586bcf4aa342d250712a92475c9e1cbb1d03a2868ddee4316e4144fe5","observation_id":"5690e52e-a7a8-4692-bc31-66e7eaea2318","resolution":{"observed_at":"2026-08-07T15:29:50.867777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:50.939093Z","title":"Automatic traceability link recovery via active learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:50.939093Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:b320e84528caa29d1cb599a6855ff402ebc3173e2acce089f8e79862a9795ac2","observation_id":"3b39ebb7-4ab8-403f-a0fe-fd8352bc7b95","resolution":{"observed_at":"2026-08-07T15:29:50.939093Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:51.067635Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:51.067635Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:eb7b16baefecc6f0bab337271cab0d8d2881e122ed7d6c71dce94c81a578ac00","observation_id":"8ce18b71-b2b6-40b6-921f-588b23120bed","resolution":{"observed_at":"2026-08-07T15:29:51.067635Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:51.172524Z","title":"Incremental approach and user feedbacks: a silver bullet for traceability recovery","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:51.172524Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:4078a29c844eafcdf2bb4866dab254010b465437a4d04d24a936d750c7eb2557","observation_id":"b71a5cea-dfe1-45a3-bb34-34addc6ca393","resolution":{"observed_at":"2026-08-07T15:29:51.172524Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:51.313595Z","title":"Ir-based traceability recovery processes: An empirical comparison of one-shot and incremental processes","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:51.313595Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:8e3d6abc1937d9b4db82f3cbab12f17f014942c8a7669b37bd781034023cdf62","observation_id":"bc1e51c9-f52a-46a8-a6b6-eb65157d747c","resolution":{"observed_at":"2026-08-07T15:29:51.313595Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:51.439764Z","title":"Assessing ir-based traceability recovery tools through controlled experiments","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:51.439764Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:edf7b72454f69b1635de6ce7726f7d7c8881e7af0ea3b749b87165fef5a8df86","observation_id":"b680a074-f97c-4821-8891-3e2cc6e91073","resolution":{"observed_at":"2026-08-07T15:29:51.439764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:51.576467Z","title":"Supporting and accelerating reproducible research in software maintenance using tracelab component library","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:51.576467Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:1c325edb12b1078aed16cecc6c0f35a4fbfe613d4895146e2d261bd1ff752097","observation_id":"610e85f6-10e0-4d7a-85b7-febce6077f5f","resolution":{"observed_at":"2026-08-07T15:29:51.576467Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:51.694410Z","title":"Configuring topic models for software engineering tasks in tracelab","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:51.694410Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:e8369e6f475705374057d82aebef0e35878790fc587d7e3ed7991ee69a9751bb","observation_id":"b6d604c1-ab65-4dd1-ac66-07aa3db1549f","resolution":{"observed_at":"2026-08-07T15:29:51.694410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:51.805400Z","title":"Integrating information retrieval, execution and link analysis algorithms to improve feature location in software","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:51.805400Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:7b43017bcd772967bbaa0d991e07ba892b21cff2ffa6600772ff4fa9ee9d5fa0","observation_id":"9bc01895-c604-4747-af81-6e2e37141289","resolution":{"observed_at":"2026-08-07T15:29:51.805400Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:51.935992Z","title":"What is artificial life today, and where should it go? Artificial Life , 30(1):1--15, 02 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:51.935992Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:3d5adaede3923d91684be5cf2fdbfc7c3f62d9ab0210a02c6d910045c9972f36","observation_id":"ce61d141-02e4-4de6-b823-004e01240d8a","resolution":{"observed_at":"2026-08-07T15:29:51.935992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.07002","last_updated":"2021-07-14T21:08:30Z","snapshot_observed_at":"2026-07-06T11:29:14.236629Z","submitted_at":"2021-07-14T21:08:30Z","title":"The Benchmark Lottery","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.07002","snapshot_observed_at":"2026-08-07T15:29:52.087848Z","title":"Gritsenko, Zhe Zhao, Neil Houlsby, Fernando Diaz, Donald Metzler, and Oriol Vinyals","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:52.087848Z"},"links":{"cited_paper":"/paper/2107.07002","citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:5452503e0d3246229f9f74c9eb3f083d997e7d8f66f7b1ab85e22c50d40a87d5","observation_id":"f704146b-2dd2-44a1-a6b9-0d52f27482c7","resolution":{"observed_at":"2026-08-07T15:29:52.087848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:52.206517Z","title":"Towards a rigorous science of interpretable machine learning, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:52.206517Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:ef990f385df5fe774b1c66c77ab3fc364ab2e850b071fa39f71d366663aefe1b","observation_id":"2e174a4b-5f49-4fbd-b541-182639832149","resolution":{"observed_at":"2026-08-07T15:29:52.206517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:52.367343Z","title":"Considerations for Evaluation and Generalization in Interpretable Machine Learning , pages 3--17","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:52.367343Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:f1b2676109348f3831c02349b72ef54b56a0db837d612f6c9b5d62e4793b0381","observation_id":"e3121f35-eb46-41d5-9809-02386be565c5","resolution":{"observed_at":"2026-08-07T15:29:52.367343Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:52.502289Z","title":"Eick, T.L","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:52.502289Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:abf48e5376aa844c60971a1c9c367a3d4786d75ecdc78aae8374d5c3575cfea4","observation_id":"2703bfa0-1055-473d-9ae4-057e5580590f","resolution":{"observed_at":"2026-08-07T15:29:52.502289Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:52.563408Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:52.563408Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:58f0812edad10fa59ee2ece02035de4032bcf2822e96ae806aa23a7101e615c2","observation_id":"09bfe1b5-f761-4417-b179-7a4d20f7fc56","resolution":{"observed_at":"2026-08-07T15:29:52.563408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:52.630274Z","title":"Estimating the number of remaining links in traceability recovery","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:52.630274Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:88b0f8a6fc0f62845c7cae1e24d98f316b1cbe017ca280a3502d6f56e75238bc","observation_id":"dfc84b97-1162-4097-96cf-d24809f0faa2","resolution":{"observed_at":"2026-08-07T15:29:52.630274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.05422","last_updated":"2019-08-26T08:41:43Z","snapshot_observed_at":"2026-07-06T07:14:28.902807Z","submitted_at":"2018-11-13T17:24:51Z","title":"Bayesian Data Analysis in Empirical Software Engineering Research","version":5},"cited_work":{"arxiv_id":"1811.05422","doi":null,"metadata_source":"pith","pith_arxiv_id":"1811.05422","snapshot_observed_at":"2026-08-07T15:30:10.925636Z","title":"Bayesian Data Analysis in Empirical Software Engineering Research","venue":"cs.SE","work_id":"2d525721-38ff-4f3c-9683-9756278addd4","year":2018},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:52.733952Z"},"links":{"cited_paper":"/paper/1811.05422","citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:38c3023692bf51ee374a2ff3d84823d30024a92cf875feb6721d1cb5dc7a7c15","observation_id":"608afb84-f239-4a12-a162-5dfa13449f93","resolution":{"observed_at":"2026-08-07T15:30:11.022900Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:52.813611Z","title":"Automated repair of programs from large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:52.813611Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:192b9c7c198c34506fb1c06076b986ea14fd11c081acabfb7f680b5ca951b655","observation_id":"b8d49597-f324-4860-ab3c-5b9c4e649dc9","resolution":{"observed_at":"2026-08-07T15:29:52.813611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:52.905261Z","title":"C ode BERT : A pre-trained model for programming and natural languages","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:52.905261Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:b472a0a9975ebdc4c2f847b57fef495cc543a5c2681ea0e9e733087529af4040","observation_id":"a0214937-7ddb-4b83-bf25-6a6c59df04b0","resolution":{"observed_at":"2026-08-07T15:29:52.905261Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:52.965335Z","title":"Vulexplainer: A transformer-based hierarchical distillation for explaining vulnerability types","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:52.965335Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:d8a4ac966ef97f85a7146beb88b64dcf292f59396e3ad2203748bcaa38faca3f","observation_id":"63405cf1-8d4f-495e-956d-f4c987da82b6","resolution":{"observed_at":"2026-08-07T15:29:52.965335Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:53.017185Z","title":"Comparing explanation methods for traditional machine learning models part 1: An overview of current methods and quantifying their disagreement","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:53.017185Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:947a8764c68ca61279e43fb43422b2f33c9f8603542a00ba81e9e8cbca7de78b","observation_id":"c7b90ff6-20ab-4781-b90f-36ca05f583b1","resolution":{"observed_at":"2026-08-07T15:29:53.017185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:53.099721Z","title":"Can cooperative multi-agent reinforcement learning boost automatic web testing? an exploratory study","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:53.099721Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:3ec9d10d2c9ab5bf1ea17db208348ca01c823b67ba3b86f083810560a007122e","observation_id":"ec00674b-f141-4065-9da7-bc725c2ce53b","resolution":{"observed_at":"2026-08-07T15:29:53.099721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:53.188353Z","title":"Trace++: A traceability approach to support transitioning to agile software engineering","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:53.188353Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:32f1fb41cb231e215598ed93729a4814e11252378d378e9ec08c5a0f65e8ad31","observation_id":"f6ea6b11-9000-43c4-ab94-feb04279c21d","resolution":{"observed_at":"2026-08-07T15:29:53.188353Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:53.272753Z","title":"The pile: An 800gb dataset of diverse text for language modeling, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:53.272753Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:1f9045636532186283932ae03fcdfa40f702f8ac65e7a3a74f7b5e7be8af0844","observation_id":"d5979c33-fbd4-4d7a-b7c3-6d6366f4da62","resolution":{"observed_at":"2026-08-07T15:29:53.272753Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:53.395901Z","title":"Codeparrot, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:53.395901Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:1c391252a9f63f05531baa2096dfaa969402bfe7a006bd31bd142c9bf8e03755","observation_id":"b344cc9b-120d-4d11-84b4-8f97aa23a447","resolution":{"observed_at":"2026-08-07T15:29:53.395901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:53.525641Z","title":"Semantically enhanced software traceability using deep learning techniques","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:53.525641Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:4353d0f06691b74c860694a0b50eefc31f04352cd08717ffa1d5576092b2fb79","observation_id":"98a0457f-74a6-4d53-8b01-0664a7c25a49","resolution":{"observed_at":"2026-08-07T15:29:53.525641Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:53.660602Z","title":"Foundations for an expert system in domain-specific traceability","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:53.660602Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:8498813e0c4a00797f8667c47d1af6effa0150e1c1b8db7409e9c57813a6806f","observation_id":"a75755e0-3d28-4e6d-9c23-84ca5c44b8b9","resolution":{"observed_at":"2026-08-07T15:29:53.660602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:53.747738Z","title":"Reconciling Manual and Automatic Refactoring","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:53.747738Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:85b899d53b1ee26252f02fb3bebf08b8f70c17f0a6325c5082ebdb50332bec3e","observation_id":"4e85f9d2-aa44-449e-bc43-aa00dd4d5ced","resolution":{"observed_at":"2026-08-07T15:29:53.747738Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:53.796208Z","title":"Github, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:53.796208Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:55011755f386f44c9ecd01ae66da8b57a26658dcf284569420b72f8b2f694b9c","observation_id":"1490c9a4-1fd2-40d4-baf2-de3084b8d7d7","resolution":{"observed_at":"2026-08-07T15:29:53.796208Z","resolver_source":null,"status":"parse_uncertain"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:53.835458Z","title":"Github copilot, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:53.835458Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:a3530789e08eda23b630b19ef1e4a38ed07f86835ee8949677b8ce4214ca776b","observation_id":"4cb58e7e-2447-414e-bb5e-0b9b7225a593","resolution":{"observed_at":"2026-08-07T15:29:53.835458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:53.918549Z","title":"Gethers, R","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:53.918549Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:e93c879b98bf5ebea4f59b0539eb569d79fbd8d1bd060d6af595b32073255b59","observation_id":"77eda7b8-a2b1-4907-9c21-ed9ec4ee8887","resolution":{"observed_at":"2026-08-07T15:29:53.918549Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:53.991635Z","title":"On integrating orthogonal information retrieval methods to improve traceability recovery","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:53.991635Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:ad36ac7642cf12db135292fb75fab3780055da5dad0c055e29677ec5a022f1e9","observation_id":"140ba880-61ac-4f65-9fee-86d2b33ef7c1","resolution":{"observed_at":"2026-08-07T15:29:53.991635Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:54.049426Z","title":"Cold-start Software Analytics","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:54.049426Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:b51a422111c63cce47101c8fbc55493b228570af5b067346f5cfbfc591c2e1c0","observation_id":"8292c0b0-ad3c-46e3-ad94-da80a9f87b69","resolution":{"observed_at":"2026-08-07T15:29:54.049426Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:54.147108Z","title":"Graphcode \\ bert \\ : Pre-training code representations with data flow","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:54.147108Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:077359b6e15aaada5f11a98bb57895889a7c5a27b656717fc3607316ed7ae928","observation_id":"af5e24e2-d852-47b3-81af-db26c43ba037","resolution":{"observed_at":"2026-08-07T15:29:54.147108Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:54.249600Z","title":"Traceability recovery between bug reports and test cases-a mozilla firefox case study","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":99,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:54.249600Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:1da6db27a3a0fe8c5e86829344d3645f8febf729081c4ecc891b51fea433529c","observation_id":"b6d078e8-7b63-4568-b285-434a87c572b0","resolution":{"observed_at":"2026-08-07T15:29:54.249600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:54.304076Z","title":"Do automatic test generation tools generate flaky tests?, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering","version":1},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:54.304076Z"},"links":{"citing_paper":"/paper/2505.15023"},"observation_digest":"sha256:45aa4ca9d88bcf930b5d9468dadc434a5c70b867d0c6bc013532940a84dd338e","observation_id":"873f1a46-32b2-4463-8c66-696f98bbd4eb","resolution":{"observed_at":"2026-08-07T15:29:54.304076Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.15023","last_updated":"2025-05-21T02:13:11Z","latest_version":1,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-07T15:23:12.316822Z","submitted_at":"2025-05-21T02:13:11Z","title":"Towards a Science of Causal Interpretability in Deep Learning for Software Engineering"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":1,"unresolved":97,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":300},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 100 of 300 outbound references and 0 inbound Pith citation observations for arXiv:2505.15023."}