{"id":"W2245294015","doi":"10.1109/issre.2015.7381799","title":"A similarity-based approach for test case prioritization using historical failure data","year":2015,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Regression testing; Computer science; Software quality; Test case; Reliability engineering; Quality assurance; Test (biology); Context (archaeology); Data mining; Code coverage; Software quality assurance; Similarity (geometry); Quality (philosophy); Fault detection and isolation; Test suite; Software; Machine learning; Artificial intelligence; Regression analysis; Software system; Software development; Engineering; Programming language; Software construction","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":[0.006638046,0.001524447,0.002171185,0.01523615,0.0007684595,0.001953429,0.002720328,0.001582829,0.001457373],"category_scores_gemma":[0.049617,0.0004964134,0.001295445,0.006823809,0.0008591343,0.003011929,0.001803947,0.00140202,0.000553161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001672658,"about_ca_system_score_gemma":0.001700507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005663417,"about_ca_topic_score_gemma":0.006542317,"domain_scores_codex":[0.9869466,0.003052604,0.001847618,0.002315746,0.00542678,0.0004106085],"domain_scores_gemma":[0.958923,0.02037077,0.006681331,0.003770956,0.009369596,0.0008842967],"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":[0.0006627094,0.0009621753,0.05799434,0.0007164893,0.0006829536,0.0003665171,0.0006815749,0.1835919,0.01767514,0.008112758,0.00273379,0.7258196],"study_design_scores_gemma":[0.00007086946,0.0007057867,0.01575933,0.00005116033,0.0001209621,0.0005901997,0.0001495823,0.9664121,0.006297652,0.007811318,0.001940993,0.00009000908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0626528,0.0005636969,0.9326885,0.000136057,0.00004381777,0.000563223,0.0005124442,0.001631117,0.001208286],"genre_scores_gemma":[0.5137359,0.0001756289,0.4831732,0.00009042289,0.00008628854,0.000507962,0.001410504,0.0001500472,0.0006702017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01523615,"threshold_uncertainty_score":0.03510576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1918004958848227,"score_gpt":0.3351835844073958,"score_spread":0.1433830885225731,"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."}}