{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005038614,0.0001663087,0.0001971577,0.00007940723,0.0004406138,0.0002232403,0.0002482701,0.000113314,0.000008611645],"category_scores_gemma":[0.0009191837,0.0001486221,0.0001062409,0.0007311247,0.00002514175,0.0005510561,0.0001005316,0.0002267845,0.000009428551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007955845,"about_ca_system_score_gemma":0.0001474682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003261571,"about_ca_topic_score_gemma":0.000003229013,"domain_scores_codex":[0.9986389,0.00001181617,0.0002391095,0.0005737994,0.000205444,0.0003309115],"domain_scores_gemma":[0.998863,0.000171285,0.0001093576,0.0001465537,0.0006160874,0.0000936518],"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":[0.00007260341,0.001329378,0.8650855,0.00447217,0.0001101094,0.0002197794,0.004154509,0.00431177,0.005060147,0.002929894,0.006796539,0.1054576],"study_design_scores_gemma":[0.0008577353,0.0003116161,0.0006944393,0.00005010444,0.00002163306,0.002482522,0.0001561987,0.9721007,0.01018744,0.0002520692,0.01252708,0.0003584903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02566636,0.0001475694,0.9722353,0.0002826188,0.0001532639,0.0003704021,0.000004082763,0.0007486963,0.0003917052],"genre_scores_gemma":[0.7274448,0.000003790088,0.2715648,0.0002132442,0.0001234966,0.000148774,0.00001273206,0.00001712006,0.0004712886],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9677889,"threshold_uncertainty_score":0.6060633,"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."}}