{"id":"W1994248747","doi":"10.1145/2597073.2597075","title":"An empirical study of just-in-time defect prediction using cross-project models","year":2014,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":178,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Leverage (statistics); Predictive modelling; Computer science; Context (archaeology); Project management; Data science; Machine learning; Artificial intelligence; Systems engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.0009778088,0.00009495788,0.0001560296,0.0002769878,0.00004169656,0.0001179843,0.0007085697,0.00005515867,0.000007100034],"category_scores_gemma":[0.0001802489,0.00008460158,0.00003631062,0.000620754,0.00002208293,0.0006341411,0.0002000845,0.0001343422,0.00000670171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006621194,"about_ca_system_score_gemma":0.00005720143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003770808,"about_ca_topic_score_gemma":0.00001089979,"domain_scores_codex":[0.9985081,0.0001464871,0.0002210742,0.0003719693,0.0005013204,0.000251073],"domain_scores_gemma":[0.9988845,0.0003076192,0.00002634186,0.0006333741,0.00008672826,0.00006145595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009277667,0.0004566737,0.5431343,0.00001310811,0.00001042635,0.000003735556,0.001252482,0.4533966,0.0007103169,0.0001070092,0.0000430283,0.0008630886],"study_design_scores_gemma":[0.0003647677,0.0004423326,0.1692889,0.000006104868,0.000002163852,0.000003655888,0.00001807014,0.8295332,0.0001962924,0.00007603097,0.000002546171,0.00006592859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5816165,0.000002615072,0.4179444,0.000001999351,0.00005016256,0.0001543047,3.6134e-7,0.0001564455,0.00007323069],"genre_scores_gemma":[0.9803095,2.314281e-7,0.01959596,0.000008320461,0.00003453642,0.000008947575,5.121566e-7,0.0000103368,0.00003167214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.398693,"threshold_uncertainty_score":0.3449952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07860207803743692,"score_gpt":0.3883394692007615,"score_spread":0.3097373911633245,"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."}}