{"id":"W1522741019","doi":"10.1002/stvr.1572","title":"Coverage‐based regression test case selection, minimization and prioritization: a case study on an industrial system","year":2015,"lang":"en","type":"article","venue":"Software Testing Verification and Reliability","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Fonds National de la Recherche Luxembourg","keywords":"Regression testing; Minification; Selection (genetic algorithm); Computer science; Prioritization; Fault detection and isolation; Suite; Reliability engineering; Test suite; Regression; Fault (geology); Regression analysis; Data mining; Machine learning; Test case; Artificial intelligence; Statistics; Engineering; Software; Mathematics; Software system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00236282,0.0004769283,0.0003753844,0.001163186,0.0005224155,0.0004736378,0.0009475021,0.0007806709,0.001036378],"category_scores_gemma":[0.009054066,0.000261037,0.0004380928,0.0008853189,0.0005641717,0.0004912508,0.0003807077,0.0004981832,0.0001413628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009127594,"about_ca_system_score_gemma":0.0007646199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007404279,"about_ca_topic_score_gemma":0.008795127,"domain_scores_codex":[0.9977835,0.001092304,0.0001004659,0.0001448867,0.0007050007,0.000173804],"domain_scores_gemma":[0.985876,0.01149689,0.0007148496,0.0006826772,0.001027153,0.0002023579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001622368,0.002581368,0.05502882,0.0008289422,0.0002315673,0.01098338,0.001896136,0.5816472,0.06314454,0.00491456,0.002932246,0.2741889],"study_design_scores_gemma":[0.000512466,0.003862686,0.02980914,0.00007534023,0.0002227781,0.003629441,0.00106072,0.8720822,0.08189059,0.001645526,0.00513286,0.00007624548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9730856,0.000136776,0.02413686,0.0001916237,0.00000695634,0.0001601305,0.00007457532,0.0002639738,0.001943458],"genre_scores_gemma":[0.9740158,0.00007177189,0.02511113,0.00002566575,0.000004507941,0.00005683756,0.00007512997,0.00002904397,0.0006099965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007404279,"threshold_uncertainty_score":0.01472235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08433280682020698,"score_gpt":0.3118294633298048,"score_spread":0.2274966565095978,"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."}}