{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001528536,0.0002350939,0.0002237536,0.0001543052,0.0006156221,0.0003189929,0.0001612534,0.0001696946,4.896787e-7],"category_scores_gemma":[0.01179017,0.0002070198,0.00001831861,0.0008144915,0.00008525265,0.000410123,0.0000805169,0.0002431698,0.000001325107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001762393,"about_ca_system_score_gemma":0.0002634479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007774481,"about_ca_topic_score_gemma":0.00001308529,"domain_scores_codex":[0.997766,0.0004723068,0.000436418,0.0008233655,0.0003086937,0.0001932843],"domain_scores_gemma":[0.9965063,0.001476215,0.0002705831,0.000704745,0.000768513,0.0002736364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003835779,0.0009103609,0.9337693,0.0001060897,0.000005535681,0.001210001,0.002284081,0.001005991,0.00001489199,0.00008063306,0.0008807874,0.05969395],"study_design_scores_gemma":[0.004431493,0.005818601,0.04676836,0.000654847,0.0001035795,0.03263719,0.001711693,0.9041228,0.0002554952,0.002105565,0.0001787512,0.001211632],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7037818,0.00002036258,0.2873782,0.00009623001,0.0001935453,0.0007524516,0.000006784828,0.007744317,0.00002628226],"genre_scores_gemma":[0.8745881,4.5007e-7,0.1251789,0.00005092412,0.00008381901,0.00006288978,0.000009478461,0.000016569,0.000008839556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9031168,"threshold_uncertainty_score":0.9965339,"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."}}