{"id":"W2515047361","doi":"10.1109/bigmm.2016.36","title":"Empirical Investigation of Code and Process Metrics for Defect Prediction","year":2016,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Eclipse; Machine learning; Software bug; Support vector machine; Artificial intelligence; Process (computing); Software metric; Component (thermodynamics); Random forest; Data mining; Software; Feature (linguistics); Binary classification; Code (set theory); Artificial neural network; Root cause; Software quality; Software development; Reliability engineering; Set (abstract data type); Programming language; 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":[0.009032534,0.0006280374,0.0004157069,0.003511406,0.0002796353,0.0007082404,0.000849724,0.0007399907,0.0008425657],"category_scores_gemma":[0.07181337,0.0002021869,0.0006839569,0.003514289,0.0005702067,0.001481209,0.0005412503,0.001174265,0.0004080097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004297112,"about_ca_system_score_gemma":0.0004436499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004614178,"about_ca_topic_score_gemma":0.004277329,"domain_scores_codex":[0.9949691,0.002782382,0.0003128088,0.0005483116,0.001162411,0.0002251524],"domain_scores_gemma":[0.8600981,0.1165054,0.01089948,0.006087464,0.005262838,0.001146686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001235648,0.0001718973,0.9553021,0.00004804119,0.000184028,0.00008679528,0.0001180408,0.01483851,0.0002400745,0.0004461664,0.0007754982,0.02766518],"study_design_scores_gemma":[0.00001413415,0.0003758452,0.7205873,0.00005145293,0.00008976305,0.000395375,0.0002677285,0.2744505,0.0009845583,0.001225666,0.001532495,0.00002513225],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886757,0.0005424927,0.008886313,0.0001659449,0.00001409208,0.00001875898,0.000739942,0.00009215478,0.0008645495],"genre_scores_gemma":[0.9973436,0.00006921637,0.001672786,0.000009304737,0.000008706083,0.00001184357,0.0007476178,0.00001262791,0.0001243703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009032534,"threshold_uncertainty_score":0.04776919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05174375538336614,"score_gpt":0.3245794560113815,"score_spread":0.2728357006280154,"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."}}