{"id":"W2316930373","doi":"10.1109/tse.2016.2550458","title":"Developer Micro Interaction Metrics for Software Defect Prediction","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Ministry of Education, Science and Technology; Neurosciences Research Foundation","keywords":"Computer science; Eclipse; Leverage (statistics); Software quality assurance; Software quality; Software bug; Software; Software metric; Software engineering; Source code; Software development; Task (project management); Plug-in; Machine learning; Operating system; Systems 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004069565,0.0003566683,0.0002676741,0.0009462849,0.0002045395,0.0001441309,0.0006619834,0.0001986958,0.00003107749],"category_scores_gemma":[0.001117358,0.0003065394,0.0002896638,0.001128671,0.00002376978,0.001008192,0.00001001807,0.0003422431,0.0001054488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005843522,"about_ca_system_score_gemma":0.0001056919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006330372,"about_ca_topic_score_gemma":0.000002184684,"domain_scores_codex":[0.9977731,0.00003014317,0.0003917169,0.0006957263,0.0004655416,0.0006438468],"domain_scores_gemma":[0.9955263,0.00323127,0.00005839698,0.0006958271,0.0002724895,0.0002157588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001578634,0.00044234,0.004060572,0.000516134,0.0007250589,0.00003666055,0.0006519851,0.297218,0.02674831,0.0003095566,0.003032913,0.6661006],"study_design_scores_gemma":[0.006996764,0.001775569,0.01575102,0.001827012,0.0002286351,0.0004690783,0.0000384999,0.09855083,0.803758,0.0002350339,0.06656913,0.003800469],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01375116,0.00008010276,0.9795155,0.00008207253,0.003240561,0.000482169,0.00008687627,0.002759245,0.000002339141],"genre_scores_gemma":[0.5481198,0.00004601073,0.4507176,0.00004546884,0.0001404619,0.0004584488,0.000004014391,0.0001005088,0.0003676242],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7770097,"threshold_uncertainty_score":0.9999387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0218172345002419,"score_gpt":0.251711406731874,"score_spread":0.2298941722316321,"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."}}