{"id":"W4400582353","doi":"10.1145/3660809","title":"Mining Action Rules for Defect Reduction Planning","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ACM on software engineering.","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Commit; Computer science; Counterfactual thinking; Reduction (mathematics); Precision and recall; Code (set theory); Action (physics); Recall; Compiler; Software; Baseline (sea); Machine learning; Software bug; Artificial intelligence; Software engineering; Programming language; Database; Set (abstract data type)","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":[0.00248963,0.001779192,0.0009589106,0.003823457,0.0007189459,0.001324581,0.001812453,0.001356769,0.00186069],"category_scores_gemma":[0.01483726,0.000628332,0.0020836,0.001446749,0.000891896,0.001558167,0.001134968,0.001616119,0.0007103562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001224173,"about_ca_system_score_gemma":0.003765405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01032147,"about_ca_topic_score_gemma":0.01794616,"domain_scores_codex":[0.996785,0.0008246491,0.000295607,0.0008252639,0.00107621,0.0001932515],"domain_scores_gemma":[0.9869556,0.009423043,0.001098933,0.0009850197,0.001331744,0.0002057131],"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":[0.0004092722,0.0007352392,0.04425475,0.001145422,0.0003545457,0.001875243,0.0008452783,0.3775247,0.01381049,0.009574661,0.01025083,0.5392195],"study_design_scores_gemma":[0.00005022698,0.0001267524,0.002225915,0.00008988295,0.0001142215,0.0002583449,0.0001957259,0.9729674,0.007828868,0.01238572,0.003722615,0.00003426463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1165589,0.001020969,0.8617915,0.001432709,0.0001131847,0.0006972683,0.003513296,0.01190358,0.002968551],"genre_scores_gemma":[0.5108278,0.0003682417,0.479359,0.0003423096,0.00003868619,0.0005673634,0.006789538,0.000388411,0.001318645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01032147,"threshold_uncertainty_score":0.02052277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03604877384191616,"score_gpt":0.2930271781068178,"score_spread":0.2569784042649016,"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."}}