{"id":"W3174824587","doi":"10.21467/proceedings.115.25","title":"A Machine Learning Based Approach for Software Test Case Selection","year":2021,"lang":"en","type":"article","venue":"AIJR Proceedings","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Computer science; Regression testing; Machine learning; Feature selection; Artificial intelligence; Software; Selection (genetic algorithm); Categorical variable; Test data; Test (biology); Task (project management); Test case; Software regression; Data mining; Software system; Software quality; Software development; Software construction; Software engineering; Regression analysis; Programming language; Engineering","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":[0.003293247,0.001253505,0.001377014,0.004670193,0.0006561407,0.001262218,0.002073864,0.001363645,0.002615956],"category_scores_gemma":[0.01181946,0.0004605822,0.001193119,0.002551355,0.000590689,0.001037173,0.0008519194,0.001622521,0.0009350806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001281339,"about_ca_system_score_gemma":0.001499577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003995882,"about_ca_topic_score_gemma":0.005552131,"domain_scores_codex":[0.9944078,0.002124102,0.0004467694,0.0008603234,0.001960177,0.0002008475],"domain_scores_gemma":[0.9908664,0.00603895,0.0004514748,0.0004779707,0.002038742,0.0001265737],"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":[0.0001357005,0.0007093565,0.005057185,0.0002192248,0.0002230747,0.0003315285,0.0001330973,0.1347895,0.009353925,0.004467311,0.004526393,0.8400537],"study_design_scores_gemma":[0.00003072678,0.0001430981,0.00126799,0.00002010944,0.0000335806,0.0002091018,0.00003393864,0.9887242,0.003616097,0.004217415,0.001683423,0.00002028946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007909958,0.0001717534,0.9890353,0.0001963,0.00003171322,0.0003369482,0.0001413311,0.001310208,0.0008664824],"genre_scores_gemma":[0.203552,0.0001323717,0.7923537,0.0003523814,0.00008620608,0.0007466778,0.0007882421,0.0001156038,0.001872864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004670193,"threshold_uncertainty_score":0.01741654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01343563284720625,"score_gpt":0.2307202064175132,"score_spread":0.217284573570307,"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."}}