{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003097755,0.001535208,0.0007964729,0.005848203,0.0003516886,0.001030721,0.0008788648,0.0006483331,0.001366178],"category_scores_gemma":[0.03382161,0.0003773572,0.0004354369,0.004054153,0.0002750664,0.001762146,0.001093075,0.0009973885,0.0005979152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000689398,"about_ca_system_score_gemma":0.0008483418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002936218,"about_ca_topic_score_gemma":0.005377657,"domain_scores_codex":[0.9939766,0.001501585,0.000413196,0.0006917517,0.003219349,0.0001974618],"domain_scores_gemma":[0.9585689,0.02210862,0.00883072,0.00383123,0.005598356,0.001062242],"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":[0.0004833993,0.0006256983,0.3305445,0.0005124491,0.000303857,0.0001768909,0.0006222825,0.06454238,0.01890505,0.003409086,0.01073459,0.5691398],"study_design_scores_gemma":[0.00005759715,0.001113147,0.1642677,0.0001404713,0.0001477123,0.0003542723,0.0002181569,0.7984732,0.01909723,0.007268052,0.008717351,0.0001450847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5418034,0.002834143,0.4268772,0.0005944048,0.0001385019,0.0004286973,0.005089963,0.01438846,0.007845254],"genre_scores_gemma":[0.8668675,0.0002765494,0.1283335,0.0000695132,0.00004411845,0.000313601,0.002823368,0.0003589007,0.000912945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005848203,"threshold_uncertainty_score":0.01638269,"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."}}