{"id":"W3197188256","doi":"10.1016/j.infsof.2021.106756","title":"Early prediction for merged vs abandoned code changes in modern code reviews","year":2021,"lang":"en","type":"preprint","venue":"Information and Software Technology","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Code review; Code (set theory); Machine learning; Classifier (UML); Artificial intelligence; Source code; Suite; Software; Source lines of code; Process (computing); Empirical research; Software inspection; Software engineering; Software development; Software quality; Programming language; Statistics","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":[0.005784745,0.0005254017,0.0006068309,0.005787952,0.001103868,0.00267644,0.0009412473,0.001808492,0.003551594],"category_scores_gemma":[0.1001189,0.0005064505,0.0008190485,0.002468363,0.000760791,0.003658534,0.001254466,0.002395466,0.001569621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009687676,"about_ca_system_score_gemma":0.001565011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002570335,"about_ca_topic_score_gemma":0.005038762,"domain_scores_codex":[0.9926662,0.0011676,0.0006206204,0.001528372,0.003241424,0.0007756897],"domain_scores_gemma":[0.8072318,0.1342861,0.02003342,0.00892669,0.02488797,0.004634019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002662119,0.0002383007,0.7893838,0.0008001067,0.0003064777,0.001267947,0.0009358509,0.005606967,0.009717418,0.007186622,0.01833954,0.163555],"study_design_scores_gemma":[0.0001788879,0.0008709149,0.7199842,0.0006468838,0.0006076207,0.00379912,0.001341626,0.1941832,0.02723275,0.02235185,0.02858998,0.0002129778],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9498756,0.004013439,0.02796943,0.001679384,0.0007028214,0.0001651667,0.004082104,0.001878383,0.009633609],"genre_scores_gemma":[0.9882761,0.0003027642,0.00683868,0.0001492527,0.0001877548,0.00003705483,0.002060662,0.0002006217,0.001947069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005787952,"threshold_uncertainty_score":0.03059304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02435593368266787,"score_gpt":0.27361690913263,"score_spread":0.2492609754499622,"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."}}