{"paper":{"title":"Method-level Change-proneness: A Better Metric for Black-box Test Suite Minimization","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"Method-level change-proneness provides a stronger guide than class-level metrics for shrinking black-box test suites.","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Kazi Sakib, Md Siam","submitted_at":"2026-05-13T18:13:58Z","abstract_excerpt":"Test Suite Minimization (TSM) reduces the size of test suites while preserving their fault detection capability. In black-box TSM, reduction is performed without analyzing production code. While several black-box TSM approaches have explored metrics like test logs or test similarity, those often suffer from scalability and efficiency issues. On the other hand, change-proneness (CP), recently emerged as an efficient and scalable alternative metric, has only been applied at class level. To accurately identify fault-revealing test cases, we propose CP at finer-grained method-level and implement M"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"MCTM achieves 0.93 accuracy and 0.94 fault detection rate on average, significantly outperforming class-level CP and similarity-based approaches while maintaining superior efficiency.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The dependency between test cases and methods can be accurately determined by analyzing the test-code call-graph, allowing reliable scoring of association with change-prone methods.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MCTM applies method-level change-proneness from version history and call-graph analysis to minimize black-box test suites, reporting 0.93 accuracy and 0.94 fault detection rate on 15 Java projects with 635 buggy versions.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Method-level change-proneness provides a stronger guide than class-level metrics for shrinking black-box test suites.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"c1ab08421071a929755d823e0760ab67a652e3467e6cd638e43b9d9841facc6f"},"source":{"id":"2605.15232","kind":"arxiv","version":1},"verdict":{"id":"38ced200-43cc-43a2-8130-3e056910feb8","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T17:13:45.133174Z","strongest_claim":"MCTM achieves 0.93 accuracy and 0.94 fault detection rate on average, significantly outperforming class-level CP and similarity-based approaches while maintaining superior efficiency.","one_line_summary":"MCTM applies method-level change-proneness from version history and call-graph analysis to minimize black-box test suites, reporting 0.93 accuracy and 0.94 fault detection rate on 15 Java projects with 635 buggy versions.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The dependency between test cases and methods can be accurately determined by analyzing the test-code call-graph, allowing reliable scoring of association with change-prone methods.","pith_extraction_headline":"Method-level change-proneness provides a stronger guide than class-level metrics for shrinking black-box test suites."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15232/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-19T17:41:56.182685Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T17:31:18.486418Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T17:26:25.399236Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.828827Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"13dc9d9b2676904bac0cced75bea493512a1c0f28d31246c120deff4c5003d86"},"references":{"count":53,"sample":[{"doi":"10.1002/stv.430","year":2012,"title":"Regression testing minimization, selection and prioritization: a survey,","work_id":"28ee0157-e0ec-4bfb-b97c-25a734f83cf9","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1109/access.2018.2809600","year":2018,"title":"A systematic review on test suite reduction: Approaches, experiment’s quality evaluation, and guidelines,","work_id":"eecbb667-2cc9-4d98-bda2-2f378372714f","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1109/icse.2019.00054","year":2019,"title":"E., & Ray, T","work_id":"3d7bbe44-fb83-47b6-a131-32663f0b507c","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1109/icse48619","year":2023,"title":"ATM: black-box test case minimization based on test code similarity and evolutionary search,","work_id":"4eb1fa99-8ef1-4d5d-ad5b-850dda7f8cc5","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Ltm: Scalable and black-box similarity-based test suite mini- mization based on language models,","work_id":"518a3e33-2147-4760-b71b-fcb8b61ea519","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":53,"snapshot_sha256":"cc18b77abc2b923ba44153af4b813403bf387b7385024614a00dc204a4f65d1f","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"0e6ee60e118139262440344fb39f24becf4efead12b37d053ffa0957560db481"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}