{"as_of":"2026-08-19T13:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:93e3d6a79ec280df52ddac79e4e9b03c888a0688e0e717d99ddc93344ead6967","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:33:29.400252Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T11:42:04.849077Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-29T11:43:23.272204Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"cited_work":{"arxiv_id":"2505.00990","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.00990","snapshot_observed_at":"2026-06-29T11:43:23.272204Z","title":"Identifying root cause of bugs by capturing changed code lines with relational graph neural networks","venue":null,"work_id":"ee746bc4-0983-4db7-a3c7-77b8edb18cd9","year":null},"citing_paper":{"arxiv_id":"2605.27880","last_updated":"2026-05-27T03:04:12Z","snapshot_observed_at":"2026-08-02T08:21:55.801171Z","submitted_at":"2026-05-27T03:04:12Z","title":"Confident Learning-based Network for Detecting Bug-Inducing Commits on SZZ with Noisy Labels","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-29T11:42:04.849077Z"},"links":{"cited_paper":"/paper/2505.00990","citing_paper":"/paper/2605.27880"},"observation_digest":"sha256:6bad6101d8a9b3c350e3b25f88478304ac4fb3de64619692d07ba9d7e1d8a7e2","observation_id":"f505c974-3682-4aa3-87ab-8126720ba45d","resolution":{"observed_at":"2026-06-29T11:43:23.274509Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.00990/citation-record","integrity":"/paper/2505.00990/integrity","json":"/paper/2505.00990/citation-record.json","paper":"/paper/2505.00990"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:32.070747Z","title":"I know what you did last summer: an investigation of how developers spend their time","venue":null,"work_id":"7dfb2477-53a9-4553-9880-688dc7127e08","year":2015},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.588381Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:cd8e69979a5d84b61606297157ac8dc19954b60931d1df845ee80d6e702b0679","observation_id":"50ae78e6-d27f-49a7-8230-6f56193dce0a","resolution":{"observed_at":"2026-08-16T04:33:32.074047Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:32.059448Z","title":null,"venue":null,"work_id":"8ad4766d-b996-4cd6-b673-9e0ee923833e","year":1986},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.593455Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:9ceb7c344aea1cd3eb8c5ad5ee46ae05e0ff02b499ac289992773c79be1bdd86","observation_id":"6922f964-51e0-4f53-9336-49b25eee6660","resolution":{"observed_at":"2026-08-16T04:33:32.063840Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.849661Z","title":"Hassan, Audris Mockus, Anand Sinha, and Naoyasu Ubayashi","venue":null,"work_id":"de4e2976-eba2-4677-9969-65feb781374e","year":2013},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.597124Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:ab31eeda711f1cb18930bd4348ee14ad6de90d4c2c39730841a497320955407d","observation_id":"2ed8c451-5269-4ba8-9a51-6f8cd0a99244","resolution":{"observed_at":"2026-08-16T04:33:32.002929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.775787Z","title":"Graph autoencoder anomaly detection for e- commerce application by contextual integrating contrast with re- construction and complementarity","venue":null,"work_id":"ed304110-62b2-4ddf-a4fb-01312211a138","year":2024},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.600854Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:6733c9b60fd9deac4dc6cc7f91b765820526731301cfc6bf0236861c4b9de5bb","observation_id":"5e15d12c-8c3a-4e6a-a873-5c4b713653eb","resolution":{"observed_at":"2026-08-16T04:33:31.779147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.765774Z","title":"Knowledge graph enhanced heterogeneous graph neural network for fake news detection","venue":null,"work_id":"9a18d69e-f1f2-4ef9-a5ce-46d6f88d125d","year":2024},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.604488Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:f7b80d027c98592d0e5322c14971bf5e353a28b8c23134128da8e6651b28195b","observation_id":"3acf70a5-64da-46b4-884a-7621d2f81355","resolution":{"observed_at":"2026-08-16T04:33:31.769487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.755664Z","title":"Anomaly detection with memory-augmented adversarial autoencoder networks for industry 5.0","venue":null,"work_id":"46d3f712-9ec3-4742-b5e8-d2d0be0c0e68","year":2023},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.608065Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:ac8ffac6da34638c9377ca6a478a6c188c7261d6f2b8d3dd892cc221d8d42826","observation_id":"cbd28eeb-bf07-4e96-814c-c6bbec668482","resolution":{"observed_at":"2026-08-16T04:33:31.759556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.744895Z","title":null,"venue":null,"work_id":"86a41621-b49a-4297-b9d8-43edeca2be0d","year":2002},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.612207Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:33f7555be22e81737a3f01b9fab95e4171f3af986a92e27f94a274f879bf8605","observation_id":"57d980f9-bfa2-42d9-8c5b-82d9fce2b937","resolution":{"observed_at":"2026-08-16T04:33:31.748086Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.735339Z","title":"James Whitehead, and Yi Zhang","venue":null,"work_id":"a863b5f6-31f1-4c1e-a516-964a0c1cf9ff","year":2008},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.615964Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:365fd4da5b1ba44e14e55795ce50537f194d7641c285c23b1765e294b74c1b78","observation_id":"a6f8af32-e9f9-4075-aed3-0a798bbf5728","resolution":{"observed_at":"2026-08-16T04:33:31.738845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.665751Z","title":"Hassan, and Shanping Li","venue":null,"work_id":"0c0c6550-ca41-470a-ab20-1da3d6e805e1","year":2021},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.619000Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:a6a688e95815b1bacae878a45eb13a4e2d894d0b547b66aa0550b089967ec912","observation_id":"bb0a4857-3b34-47ef-b180-4c0d4b1b3fd1","resolution":{"observed_at":"2026-08-16T04:33:31.728369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.562019Z","title":null,"venue":null,"work_id":"3b25307b-938e-4e0a-a653-eeb25b372acc","year":2017},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.622463Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:1f8956ebb5bd85c68c46754f506210b0c5e2bd2993f0d9a03a545f6bc309cb27","observation_id":"69b476b7-14d0-43a3-ad48-5ef4e57f7607","resolution":{"observed_at":"2026-08-16T04:33:31.565156Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.551756Z","title":"James Jr","venue":null,"work_id":"a9496c2e-3e51-475a-9984-03730aa06831","year":2006},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.635559Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:240eb4819fa1e5692ebb1b7affcc9668b7a865adecb980523c1464c3e49fedcd","observation_id":"317f7a1e-0710-4f17-b7f5-7f0818043030","resolution":{"observed_at":"2026-08-16T04:33:31.555941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.542076Z","title":null,"venue":null,"work_id":"5ca30482-901a-4ad1-b59c-57fcdcd48b0e","year":2010},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.737291Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:605709f132ad7480fc7ba1205249016c0a9b987ccce91816911e3a0dbd0c48d6","observation_id":"6b2a0744-ac61-4343-a0b8-27c7b3582d29","resolution":{"observed_at":"2026-08-16T04:33:31.545124Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.531358Z","title":"When do changes induce fixes? ACM sigsoft software engineering notes , 30(4):1–5, 2005","venue":null,"work_id":"3531b2f2-d94e-4b0a-adc9-9bd32a6a6368","year":2005},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.805325Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:29e04d82c1472ba63e34d76da470ec5edd899de2ba8ce55cb840ad91efbace7c","observation_id":"7fe0bc21-4a2c-47a8-a0ba-310a83f811d7","resolution":{"observed_at":"2026-08-16T04:33:31.535293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.521801Z","title":"The impact of refactoring changes on the szz algorithm: An empirical study","venue":null,"work_id":"f38dfd55-d2a3-4485-9b74-b6f6106bd5c2","year":2018},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.885891Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:7a3a975a5690f8b62d6d2addb2b47d6cfb62e8d417a6865d33c26a07773bf512","observation_id":"0a271729-110b-4baf-a34c-ba851680ce81","resolution":{"observed_at":"2026-08-16T04:33:31.525032Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.317338Z","title":"Neural szz algorithm","venue":null,"work_id":"9896feef-a6f3-4dbd-9c84-bbdeaca60773","year":2023},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.890676Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:1739d3cdf5a35eb0bd69d28ea87a3e136b667b769fe72cb648688b12f6331c3e","observation_id":"d5d380e9-d363-49de-a60b-acba95576641","resolution":{"observed_at":"2026-08-16T04:33:31.437141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.243160Z","title":"Mining heterogeneous information networks","venue":null,"work_id":"853a3892-a1b2-4e77-bdd6-68b68838c5f8","year":2013},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.894474Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:5500b99d6879e6670caa87c6382b215bb80fcbb3c18a7cc10660e41349d7c265","observation_id":"f43e4dad-75cb-4140-b005-1c10cb61e130","resolution":{"observed_at":"2026-08-16T04:33:31.246543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.233187Z","title":"Kipf, Peter Bloem, Rianne van·den Berg, Ivan Titov, and Max Welling","venue":null,"work_id":"09108d49-2d7f-4d43-8855-a3887f77ead0","year":2018},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.898288Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:bda04caca8861d62dc7178f0d744ddf8530f4dceb4b7096211a2a09f2d40ca44","observation_id":"c7007bf8-3eee-4cb2-9211-e643aea52c02","resolution":{"observed_at":"2026-08-16T04:33:31.237043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.223001Z","title":"Exploring and exploiting the correlations between bug-inducing and bug-fixing commits","venue":null,"work_id":"894d8552-bbcd-4c0c-9b63-96ef34a3a5bc","year":2019},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.900679Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:42ff0090dd097a9a78b5015d39885cff9cc73398727ebede7c8c4ed7b72f26d6","observation_id":"1bf19c30-1d42-4aa3-9511-151870b38836","resolution":{"observed_at":"2026-08-16T04:33:31.226559Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:31.213270Z","title":"Regminer: Towards constructing a large regression dataset from code evolution history","venue":null,"work_id":"8b86cd9d-a7c4-470f-b077-dbeefc28f61e","year":2021},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.903311Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:4a6b88abc11fb0cdd35f244f47fc3ebe633afb3125564e9b5dccf5104191d177","observation_id":"8e8caa44-c7b3-415f-8478-740248cbd4a9","resolution":{"observed_at":"2026-08-16T04:33:31.216770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:30.957240Z","title":"Revisiting and improving szz implementations","venue":null,"work_id":"cce7f576-c37e-4248-ad7e-c0ea0e3673ec","year":2019},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.906761Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:0415afdcc0a1427e5350f4acbff823d60fafbb83607dbe47e5cc4c7e436e94a9","observation_id":"3e8e2bd6-fa4c-4e4c-b292-a97fc554641a","resolution":{"observed_at":"2026-08-16T04:33:31.069116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:30.801732Z","title":null,"venue":null,"work_id":"0006678d-97ed-44c6-8dca-359095adad0b","year":1970},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.909822Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:cc124f82c594ee7fa4d16ae96d058532175899c7d2747c4f0b36b7cd9dabcd1a","observation_id":"a43986ad-848d-40d4-bd94-392aa7c9ae30","resolution":{"observed_at":"2026-08-16T04:33:30.853963Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:30.788874Z","title":"Ottenstein, and Joe D","venue":null,"work_id":"abc74787-6af1-410d-bb21-0613da60b457","year":1987},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.913229Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:ede8f56aa1b6ab66a664b6f13eaaaa6fa722a32ea136df4c4d9882f5d61d3b56","observation_id":"563c89de-4fdf-4829-ac11-c15ee329d14c","resolution":{"observed_at":"2026-08-16T04:33:30.793187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:30.775612Z","title":null,"venue":null,"work_id":"3eb6a8c9-162e-4f61-9b6e-5b39df9dc19e","year":1979},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.916504Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:f3244ed5ccd64c9fe57957a6e666389737710b7688fd5dcc54eccbba10994a95","observation_id":"faf787ad-78d9-40c4-9306-3162a0a6a680","resolution":{"observed_at":"2026-08-16T04:33:30.779787Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:30.709135Z","title":"Codebert: A pre-trained model for programming and natural languages","venue":null,"work_id":"81cbba46-c574-499b-8253-afc1cfe68b77","year":2020},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.919050Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:dbab63c99e3fd5e14bf410bdb90ee8468dff821111ec6b5de4c933d081405a9a","observation_id":"26ff3e7e-cd86-48cd-9c8d-eb926253cb82","resolution":{"observed_at":"2026-08-16T04:33:30.767957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:30.498918Z","title":"Attention is all you need","venue":null,"work_id":"f216b31b-e4b3-4227-a3a6-9b28e6149093","year":2017},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.922326Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:5d82a14e979aae1e5f315945afcd82055f21c9b3fb9aa15fa6db5c6aa6e42013","observation_id":"ff466f1e-6a0b-4edf-9389-36ae78d3ffc7","resolution":{"observed_at":"2026-08-16T04:33:30.599929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-08-14T18:16:28.847993Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-16T04:33:28.925771Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.925771Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:f768ccd387d3c7a709feb816b3f97116908d61a555fa7ff3e72240a22fe1d5cd","observation_id":"08d7d177-b58d-4203-a36c-4697a28b30e3","resolution":{"observed_at":"2026-08-16T04:33:28.925771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.10555","last_updated":"2020-03-23T21:17:42Z","snapshot_observed_at":"2026-08-11T12:55:51.308870Z","submitted_at":"2020-03-23T21:17:42Z","title":"ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.10555","snapshot_observed_at":"2026-08-16T04:33:28.928817Z","title":"Electra: Pre-training text encoders as discriminators rather than generators","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.928817Z"},"links":{"cited_paper":"/paper/2003.10555","citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:8ea7664f0e96dbae31dbf53661a2741071d15d1dc65adebf379289923c06f346","observation_id":"02795ece-257f-4671-a89f-59c579804831","resolution":{"observed_at":"2026-08-16T04:33:28.928817Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:30.442014Z","title":"Semi-supervised classification with graph convolutional networks","venue":null,"work_id":"efe31368-fee9-4b44-8d26-65750cbca122","year":2016},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.931756Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:cb5790fd8df1ab2d1c8a83ea9cf457fcf0d1f23e17979db0204b1d6ea63b6703","observation_id":"83ca2b19-7f27-4dd2-9a0b-d7cfbc2e7d40","resolution":{"observed_at":"2026-08-16T04:33:30.446162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:30.431156Z","title":null,"venue":null,"work_id":"8a3632ba-c7db-4c16-9cb7-a05892cf844b","year":2015},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.934761Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:d7e8e78dfbd6d5c857c7d439d3951068785872cee0443d8d399f33fdd2a8c016","observation_id":"891bdeb7-9596-46f3-8f95-88977b6e963a","resolution":{"observed_at":"2026-08-16T04:33:30.435055Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:30.418154Z","title":"Scarselli, M","venue":null,"work_id":"1f833894-cf95-47f3-8c64-6152d97050ad","year":2009},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.938111Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:7bcdfce01cafed33dfe3e114dcd67966f01794b32d97db9573b54b888e59067e","observation_id":"c5ce8c8f-a896-489c-b32e-6ec8b9c63921","resolution":{"observed_at":"2026-08-16T04:33:30.423334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:30.405696Z","title":"Understanding the difficulty of training deep feedforward neural networks","venue":null,"work_id":"23a1a66b-51b0-4a01-9229-3b43dd9d2c7e","year":2010},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:28.941376Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:ffb1ef27a7c9fe92b39ede0a1198ea51bc20855aa8c478f87e43d96ad1c499bd","observation_id":"913316fe-2931-4b48-ae61-909afdfa7cf7","resolution":{"observed_at":"2026-08-16T04:33:30.409611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:30.213094Z","title":null,"venue":null,"work_id":"2562fdea-06c1-4671-944b-ab8521ba11d1","year":2010},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.004936Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:10db5a41842a241c0c0afb4c92f2406da57152f2ef60acfab36c97f5093f4787","observation_id":"84d504ba-4e76-40de-9a3f-4f36003afae9","resolution":{"observed_at":"2026-08-16T04:33:30.330554Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.02002","last_updated":"2018-02-07T18:44:44Z","snapshot_observed_at":"2026-08-17T00:09:26.216715Z","submitted_at":"2017-08-07T06:32:42Z","title":"Focal Loss for Dense Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.02002","snapshot_observed_at":"2026-08-16T04:33:29.047811Z","title":"Focal loss for dense object detection","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.047811Z"},"links":{"cited_paper":"/paper/1708.02002","citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:5ac2cb5f23d9e50924e08452131d1fc318150f10f204a2cf07a6c038d90452a4","observation_id":"8da4546a-b7f8-4400-ba47-78cdfafbf65b","resolution":{"observed_at":"2026-08-16T04:33:29.047811Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:30.071297Z","title":"Are automated debugging techniques actually helping programmers? In Proceedings of the 2011 International Symposium on Software Testing and Analysis , Jul 2011","venue":null,"work_id":"6b2c7026-cdea-4ebe-800e-4caa91f17fb6","year":2011},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.139137Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:1dc5bc914488a05a8e42230d9443eebd99a046a0dd248508d4645e71d40c17d4","observation_id":"a39e70ad-47a6-485f-8169-5b4da311b9f3","resolution":{"observed_at":"2026-08-16T04:33:30.074575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:30.059760Z","title":"Speech recognition with deep recurrent neural networks","venue":null,"work_id":"d68df0cb-5989-4a2d-9eba-838e88e6d770","year":2013},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.206957Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:57a4fa95c87197a48dfeefa19f02de9182ea7a14382576456bcdca1fe4305a2e","observation_id":"8fcd067a-94b2-4da9-8481-c43e1b99e4b6","resolution":{"observed_at":"2026-08-16T04:33:30.062715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:30.000352Z","title":"Locating the security patches for disclosed oss vulnerabilities with vulnerability-commit correlation ranking","venue":null,"work_id":"89dadae2-98d3-4ce1-8edb-50f782853dfa","year":2021},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.235836Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:c999c631be9582d18e4aae9bd4c3fb0a981a89c4db2d565bae40a2a1a7df5ac6","observation_id":"91f462d7-8f2e-4f24-8b59-18eaa2fdfc27","resolution":{"observed_at":"2026-08-16T04:33:30.050688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:29.744407Z","title":"Estimation of prediction error by using k-fold cross-validation","venue":null,"work_id":"37a0a592-1b96-4426-8a9c-516374c2c2a7","year":2011},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.241478Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:2532c7ec1c2aa29484f0bf1209b7ea984b406cc76bd0694177931a8f230eda02","observation_id":"38c2015a-9907-46e1-94d6-de0cb35f01d0","resolution":{"observed_at":"2026-08-16T04:33:29.846817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.11942","last_updated":"2020-02-09T03:00:18Z","snapshot_observed_at":"2026-08-18T01:27:11.590348Z","submitted_at":"2019-09-26T07:06:13Z","title":"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.11942","snapshot_observed_at":"2026-08-16T04:33:29.244613Z","title":"Albert: A lite bert for self-supervised learning of language representations","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.244613Z"},"links":{"cited_paper":"/paper/1909.11942","citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:e322ebe6858c86eebfe112151b0874f9c4e9c65009fdcd44ebbaf8b6cdefd97e","observation_id":"9cf74c58-f5dc-4db2-b26d-e821afa883ec","resolution":{"observed_at":"2026-08-16T04:33:29.244613Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.01108","last_updated":"2020-03-01T02:57:50Z","snapshot_observed_at":"2026-08-07T19:07:36.327251Z","submitted_at":"2019-10-02T17:56:28Z","title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.01108","snapshot_observed_at":"2026-08-16T04:33:29.248796Z","title":"Distilbert, a distilled version of bert: Smaller, faster, cheaper and lighter","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.248796Z"},"links":{"cited_paper":"/paper/1910.01108","citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:3dd7e9132d238e331aca5bec69643f8ca8fcd1bd0cbd797117470cad00b3f0c6","observation_id":"fc696368-be9c-4091-9511-355b4e289b5a","resolution":{"observed_at":"2026-08-16T04:33:29.248796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-08-16T14:33:50.657682Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-08-16T04:33:29.252405Z","title":"Roberta: A robustly optimized bert pretraining ap- proach","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.252405Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:5fa07f11c18257409bd26a303e3a15505c1ef5fcbe73b42886a624a578a1fc64","observation_id":"63b86bec-05c3-42f9-be5f-eb1c321d4141","resolution":{"observed_at":"2026-08-16T04:33:29.252405Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:29.691421Z","title":"Graph Attention Networks , page 39–41","venue":null,"work_id":"8a9c13e3-3b91-4181-9eb6-b2c90c44f3df","year":2020},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.255887Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:edbc0acc35a0ead51ceda186c7fcf3f180cd1b069c7c6da2f51a18f3ec398e32","observation_id":"16b3ca45-037d-4ca7-a65d-f606bbe36bbd","resolution":{"observed_at":"2026-08-16T04:33:29.694998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:29.680389Z","title":"Human pose prediction using interpretable graph convolutional network for smart home","venue":null,"work_id":"9ce4eb6d-058f-4efc-bc21-85c403e143a5","year":2024},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.259750Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:18f7a24141339619742de7dda5341202c58b1e711c8f6f8edbfed9ecf98b871b","observation_id":"54d26779-c387-4927-893a-36c6a6602687","resolution":{"observed_at":"2026-08-16T04:33:29.684202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:29.668916Z","title":"Fcgcn: Feature correlation graph convolution network for few-shot individual identification","venue":null,"work_id":"86cfd079-c5a2-486a-a673-af9c1df7ab1b","year":2023},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.264359Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:c8df6de1a99f88394e7dbd56a2601a221d9e5fd856a5b0c6b65ff4264883b966","observation_id":"92ca41bb-7c21-40cb-89ba-18dbab7f82db","resolution":{"observed_at":"2026-08-16T04:33:29.671813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05811","last_updated":"2019-04-11T16:11:36Z","snapshot_observed_at":"2026-08-14T16:47:55.984747Z","submitted_at":"2019-04-11T16:11:36Z","title":"Relational Graph Attention Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05811","snapshot_observed_at":"2026-08-16T04:33:29.267750Z","title":"Relational graph attention networks","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.267750Z"},"links":{"cited_paper":"/paper/1904.05811","citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:a86455186cd6145c46610bdc6212a7b939bf2ea4c930f540d813edc5260632e2","observation_id":"f33de028-8592-494c-9fd5-f3bde81c0716","resolution":{"observed_at":"2026-08-16T04:33:29.267750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07739","last_updated":"2020-06-13T23:00:22Z","snapshot_observed_at":"2026-08-16T06:19:36.364672Z","submitted_at":"2020-06-13T23:00:22Z","title":"DeeperGCN: All You Need to Train Deeper GCNs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07739","snapshot_observed_at":"2026-08-16T04:33:29.270911Z","title":"Deepergcn: All you need to train deeper gcns","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.270911Z"},"links":{"cited_paper":"/paper/2006.07739","citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:75c4630d5d46164024c57f35e556096dcc3f53fb7a07858935c3d7b5d6b3495d","observation_id":"c4fba6ce-ebf9-4fdd-a856-7a74c2e89695","resolution":{"observed_at":"2026-08-16T04:33:29.270911Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:29.658268Z","title":"Addressing imbalance in multi-label classification using weighted cross entropy loss function","venue":null,"work_id":"28a234b7-02f5-43b8-a876-2e37ba119e0e","year":2020},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.274297Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:80f3af425ccde94e07fa85a7ed530bd507350f131a9d8a21b1c4318e5d51c8e9","observation_id":"2ce299a8-1989-42a2-88b1-bb52f7cf3498","resolution":{"observed_at":"2026-08-16T04:33:29.661518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:29.648332Z","title":"Gradient harmonized single-stage detector","venue":null,"work_id":"c429bc2e-65d3-4fcd-99a3-a01f963bd64f","year":2019},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.277452Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:934a1a98b1852431ba98a79d9eed98429aac0557994394d8aec2808e3fc0baaa","observation_id":"41c897ea-69da-4efb-a89f-18ef67e68298","resolution":{"observed_at":"2026-08-16T04:33:29.651091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-16T04:33:29.280830Z","title":"Binary cross entropy with deep learning technique for image classification","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.280830Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:9915d52150df220c773ffb200b7294dbff2f8d99b734db660391c47863da4fd1","observation_id":"c6ca4376-c8eb-4a0e-bbcf-bb73425d6f13","resolution":{"observed_at":"2026-08-16T04:33:29.280830Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:29.633550Z","title":null,"venue":null,"work_id":"3cf4edee-552a-4ae3-81cb-60cf80d09924","year":2010},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.320810Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:40816cf5ec08640994debf5826351d3fe163a22bdd922049bc7830a4907e01cc","observation_id":"8f6d226f-6a62-4878-9c28-d2c5b7d42f55","resolution":{"observed_at":"2026-08-16T04:33:29.637079Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:33:29.621743Z","title":"Genprog: A generic method for automatic software repair","venue":null,"work_id":"74277297-f292-4fed-a1bd-6501c8982604","year":2011},"citing_paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-16T04:33:29.400252Z"},"links":{"citing_paper":"/paper/2505.00990"},"observation_digest":"sha256:bb46b536bedbf4ebd4fde4130ed81bae4c53b163517a0866cd479a83df70f7bb","observation_id":"5217222e-44cb-4171-b2f9-a037d6a91975","resolution":{"observed_at":"2026-08-16T04:33:29.626733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.00990","last_updated":"2025-05-02T04:29:09Z","latest_version":1,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-18T17:07:12.167720Z","submitted_at":"2025-05-02T04:29:09Z","title":"Identifying Root Cause of bugs by Capturing Changed Code Lines with Relational Graph Neural Networks"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":0,"verified_fuzzy":32},"total_outbound_references":50},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2505.00990."}