{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:QK7I7B65U3WHO46TJN6FDVUZAD","short_pith_number":"pith:QK7I7B65","schema_version":"1.0","canonical_sha256":"82be8f87dda6ec7773d34b7c51d69900e167758abc1ae5818bbee63c6fc76261","source":{"kind":"arxiv","id":"1803.07764","version":1},"attestation_state":"computed","paper":{"title":"Estimating defectiveness of source code: A predictive model using GitHub content","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SE","authors_text":"Balwinder Sodhi, Ritu Kapur","submitted_at":"2018-03-21T06:31:17Z","abstract_excerpt":"Two key contributions presented in this paper are: i) A method for building a dataset containing source code features extracted from source files taken from Open Source Software (OSS) and associated bug reports, ii) A predictive model for estimating defectiveness of a given source code. These artifacts can be useful for building tools and techniques pertaining to several automated software engineering areas such as bug localization, code review, and recommendation and program repair.\n  In order to achieve our goal, we first extract coding style information (e.g. related to programming language"},"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":"1803.07764","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2018-03-21T06:31:17Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0d9268fee98b35009706ffc605bdfaf36bfbdaa2c06d808f116fa1564673a080","abstract_canon_sha256":"692138b1e61f7613f4b134ff2e6fd6c78ca7872ac372bb33816f06998af260bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:56.266760Z","signature_b64":"oq7vtNYQ1jCpR7RQ7tUAgmQq5Bdi+mtSrAgJCHX24LBpmw0GrSFPODnDoPr66xmYfNx+6VfR3EM3lA3KzrEmBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82be8f87dda6ec7773d34b7c51d69900e167758abc1ae5818bbee63c6fc76261","last_reissued_at":"2026-05-18T00:05:56.266148Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:56.266148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimating defectiveness of source code: A predictive model using GitHub content","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SE","authors_text":"Balwinder Sodhi, Ritu Kapur","submitted_at":"2018-03-21T06:31:17Z","abstract_excerpt":"Two key contributions presented in this paper are: i) A method for building a dataset containing source code features extracted from source files taken from Open Source Software (OSS) and associated bug reports, ii) A predictive model for estimating defectiveness of a given source code. These artifacts can be useful for building tools and techniques pertaining to several automated software engineering areas such as bug localization, code review, and recommendation and program repair.\n  In order to achieve our goal, we first extract coding style information (e.g. related to programming language"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.07764","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":""},"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":"1803.07764","created_at":"2026-05-18T00:05:56.266247+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.07764v1","created_at":"2026-05-18T00:05:56.266247+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.07764","created_at":"2026-05-18T00:05:56.266247+00:00"},{"alias_kind":"pith_short_12","alias_value":"QK7I7B65U3WH","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"QK7I7B65U3WHO46T","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"QK7I7B65","created_at":"2026-05-18T12:32:46.962924+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.00562","citing_title":"Towards Better Linux Kernel Fault Localization: Leveraging Contrastive Reasoning and Hierarchical Context Analysis","ref_index":38,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QK7I7B65U3WHO46TJN6FDVUZAD","json":"https://pith.science/pith/QK7I7B65U3WHO46TJN6FDVUZAD.json","graph_json":"https://pith.science/api/pith-number/QK7I7B65U3WHO46TJN6FDVUZAD/graph.json","events_json":"https://pith.science/api/pith-number/QK7I7B65U3WHO46TJN6FDVUZAD/events.json","paper":"https://pith.science/paper/QK7I7B65"},"agent_actions":{"view_html":"https://pith.science/pith/QK7I7B65U3WHO46TJN6FDVUZAD","download_json":"https://pith.science/pith/QK7I7B65U3WHO46TJN6FDVUZAD.json","view_paper":"https://pith.science/paper/QK7I7B65","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.07764&json=true","fetch_graph":"https://pith.science/api/pith-number/QK7I7B65U3WHO46TJN6FDVUZAD/graph.json","fetch_events":"https://pith.science/api/pith-number/QK7I7B65U3WHO46TJN6FDVUZAD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QK7I7B65U3WHO46TJN6FDVUZAD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QK7I7B65U3WHO46TJN6FDVUZAD/action/storage_attestation","attest_author":"https://pith.science/pith/QK7I7B65U3WHO46TJN6FDVUZAD/action/author_attestation","sign_citation":"https://pith.science/pith/QK7I7B65U3WHO46TJN6FDVUZAD/action/citation_signature","submit_replication":"https://pith.science/pith/QK7I7B65U3WHO46TJN6FDVUZAD/action/replication_record"}},"created_at":"2026-05-18T00:05:56.266247+00:00","updated_at":"2026-05-18T00:05:56.266247+00:00"}