{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004703614,0.0002030174,0.0001752647,0.0003431459,0.00009389409,0.0002319975,0.001798451,0.00009824544,0.00000165606],"category_scores_gemma":[0.008045177,0.000165793,0.0002051721,0.0006736175,0.00002250582,0.0005215806,0.000489644,0.0002837774,0.000007544852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000140806,"about_ca_system_score_gemma":0.0000423584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002156262,"about_ca_topic_score_gemma":2.897315e-8,"domain_scores_codex":[0.9985873,0.000002851307,0.0002207667,0.0004294576,0.0004071672,0.0003524325],"domain_scores_gemma":[0.9983896,0.0008546669,0.00005738392,0.0004740752,0.0001570162,0.00006727249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003567848,0.000409229,0.04344377,0.01937412,0.001807141,0.00002346207,0.01374844,0.1682593,0.2405514,0.09262162,0.2020112,0.2173936],"study_design_scores_gemma":[0.001353745,0.001288227,0.05295556,0.007649121,0.0001813774,0.0004841935,0.0002872847,0.2940084,0.5829883,0.007508895,0.04905187,0.002242986],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7944744,0.0008306714,0.1988223,0.0005002354,0.002558299,0.0004694283,0.00000701317,0.002313969,0.00002362419],"genre_scores_gemma":[0.7606502,0.000008020535,0.2385775,0.00001323864,0.0003481307,0.0001458621,0.00000174573,0.00006310101,0.0001921673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3424369,"threshold_uncertainty_score":0.9631409,"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."}}