{"id":"W4403536012","doi":"10.1145/3691620.3695551","title":"Efficient Incremental Code Coverage Analysis for Regression Test Suites","year":2024,"lang":"en","type":"article","venue":"","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Regression testing; Code coverage; Programming language; Code (set theory); Test (biology); Regression analysis; Parallel computing; Machine learning; Software; Software development","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002157812,0.001529022,0.001152209,0.005210008,0.0005233707,0.0013778,0.002058259,0.0007478181,0.003176835],"category_scores_gemma":[0.02449766,0.000584402,0.001545208,0.001913396,0.0008700863,0.001806143,0.001642612,0.00133475,0.001085716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001072992,"about_ca_system_score_gemma":0.001773044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004064418,"about_ca_topic_score_gemma":0.004752127,"domain_scores_codex":[0.9949229,0.001328325,0.0002652082,0.0006489964,0.002317263,0.0005172686],"domain_scores_gemma":[0.9807027,0.01291382,0.001420915,0.001696057,0.002931999,0.0003345404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006774716,0.0003731619,0.02073464,0.0007231883,0.0002231228,0.0007423527,0.0003164218,0.2516385,0.06401573,0.01425626,0.007466273,0.6388329],"study_design_scores_gemma":[0.00006068381,0.0001889879,0.003250758,0.0000571693,0.00007407181,0.000263239,0.00005008547,0.9615894,0.02000523,0.01201646,0.002417079,0.0000269102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08255976,0.0007609864,0.8986915,0.0002485075,0.00004839276,0.0003501848,0.000627835,0.01373,0.00298291],"genre_scores_gemma":[0.5836295,0.0003143711,0.4081354,0.0001878625,0.00009361131,0.0005242425,0.00363998,0.001502345,0.001972692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005210008,"threshold_uncertainty_score":0.01141173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02530939162931477,"score_gpt":0.3040384391541011,"score_spread":0.2787290475247863,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}