{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:6GLPYUBMLZPPMIB2D2NOTSHY3M","short_pith_number":"pith:6GLPYUBM","schema_version":"1.0","canonical_sha256":"f196fc502c5e5ef6203a1e9ae9c8f8db05b7f873cf1b035ab671f2a409c1c871","source":{"kind":"arxiv","id":"2607.02782","version":1},"attestation_state":"computed","paper":{"title":"A Preliminary Study on Explaining Risk of Code Changes using LLM-Based Prediction Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Akshay Patel, Audris Mockus, Cong Zhang, David Khavari, Jun Ge, Kosay Jabre, Nachiappan Nagappan, Peter C. Rigby, Rui Abreu, Vijayaraghavan Murali, Weiyan Sun, Yalin Liu, Zachariah J. Carmichael","submitted_at":"2026-07-02T21:31:09Z","abstract_excerpt":"Predictions by machine learning (ML) and artificial intelligence (AI) models are often received skeptically unless they are paired with intelligible explanations. In the context of just-in-time defect prediction, highlighting small portions of a software change (diff) -- beyond rule-based lints -- where risk may be concentrated has not yet been extensively investigated. In this work, we leverage attention weights from an LLM-based Diff Risk Score (DRS) model to highlight parts of a diff that the model focuses on when predicting risk. We aggregate token-level attention into interpretable code u"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.02782","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-02T21:31:09Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3936b19648a84734686f7151ca80c99d9e6d16c30009119087f0a2bb9a20d570","abstract_canon_sha256":"b6d48484a279ebfd956221449b0fa80641b5fd690fe33f3db03c6e0cd38f75f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T00:16:17.131947Z","signature_b64":"HpNDcq7Ckr8YpuRzQTLEBTOnIvxRQEC5TDGyQHl40N2V1CoYx/mCZlRU/kCkF680vgWTE6w2sYXQL9BYmbGWDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f196fc502c5e5ef6203a1e9ae9c8f8db05b7f873cf1b035ab671f2a409c1c871","last_reissued_at":"2026-07-07T00:16:17.131213Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T00:16:17.131213Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Preliminary Study on Explaining Risk of Code Changes using LLM-Based Prediction Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Akshay Patel, Audris Mockus, Cong Zhang, David Khavari, Jun Ge, Kosay Jabre, Nachiappan Nagappan, Peter C. Rigby, Rui Abreu, Vijayaraghavan Murali, Weiyan Sun, Yalin Liu, Zachariah J. Carmichael","submitted_at":"2026-07-02T21:31:09Z","abstract_excerpt":"Predictions by machine learning (ML) and artificial intelligence (AI) models are often received skeptically unless they are paired with intelligible explanations. In the context of just-in-time defect prediction, highlighting small portions of a software change (diff) -- beyond rule-based lints -- where risk may be concentrated has not yet been extensively investigated. In this work, we leverage attention weights from an LLM-based Diff Risk Score (DRS) model to highlight parts of a diff that the model focuses on when predicting risk. We aggregate token-level attention into interpretable code u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.02782","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.02782/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.02782","created_at":"2026-07-07T00:16:17.131325+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.02782v1","created_at":"2026-07-07T00:16:17.131325+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.02782","created_at":"2026-07-07T00:16:17.131325+00:00"},{"alias_kind":"pith_short_12","alias_value":"6GLPYUBMLZPP","created_at":"2026-07-07T00:16:17.131325+00:00"},{"alias_kind":"pith_short_16","alias_value":"6GLPYUBMLZPPMIB2","created_at":"2026-07-07T00:16:17.131325+00:00"},{"alias_kind":"pith_short_8","alias_value":"6GLPYUBM","created_at":"2026-07-07T00:16:17.131325+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6GLPYUBMLZPPMIB2D2NOTSHY3M","json":"https://pith.science/pith/6GLPYUBMLZPPMIB2D2NOTSHY3M.json","graph_json":"https://pith.science/api/pith-number/6GLPYUBMLZPPMIB2D2NOTSHY3M/graph.json","events_json":"https://pith.science/api/pith-number/6GLPYUBMLZPPMIB2D2NOTSHY3M/events.json","paper":"https://pith.science/paper/6GLPYUBM"},"agent_actions":{"view_html":"https://pith.science/pith/6GLPYUBMLZPPMIB2D2NOTSHY3M","download_json":"https://pith.science/pith/6GLPYUBMLZPPMIB2D2NOTSHY3M.json","view_paper":"https://pith.science/paper/6GLPYUBM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.02782&json=true","fetch_graph":"https://pith.science/api/pith-number/6GLPYUBMLZPPMIB2D2NOTSHY3M/graph.json","fetch_events":"https://pith.science/api/pith-number/6GLPYUBMLZPPMIB2D2NOTSHY3M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6GLPYUBMLZPPMIB2D2NOTSHY3M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6GLPYUBMLZPPMIB2D2NOTSHY3M/action/storage_attestation","attest_author":"https://pith.science/pith/6GLPYUBMLZPPMIB2D2NOTSHY3M/action/author_attestation","sign_citation":"https://pith.science/pith/6GLPYUBMLZPPMIB2D2NOTSHY3M/action/citation_signature","submit_replication":"https://pith.science/pith/6GLPYUBMLZPPMIB2D2NOTSHY3M/action/replication_record"}},"created_at":"2026-07-07T00:16:17.131325+00:00","updated_at":"2026-07-07T00:16:17.131325+00:00"}