{"id":"W1507777432","doi":"10.1609/aimag.v32i2.2348","title":"AI‐Based Software Defect Predictors: Applications and Benefits in a Case Study","year":2011,"lang":"en","type":"article","venue":"AI Magazine","topic":"Software Engineering Research","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Process (computing); Software; Computer science; Software bug; Code (set theory); Reliability engineering; Software engineering; Software inspection; Predictive modelling; Software development; Machine learning; Artificial intelligence; Software quality; Engineering; Operating system; Programming language","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":[0.002706512,0.0007458736,0.0004114006,0.001426752,0.0004711158,0.0007249187,0.00114947,0.001416593,0.001409956],"category_scores_gemma":[0.01091012,0.000313199,0.0004756611,0.00156948,0.0006966528,0.0009460147,0.0006939909,0.0008553006,0.0002189996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001011786,"about_ca_system_score_gemma":0.0006585323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01324624,"about_ca_topic_score_gemma":0.01524505,"domain_scores_codex":[0.998525,0.0009562677,0.00007975546,0.0001220746,0.0002526642,0.00006427843],"domain_scores_gemma":[0.9807334,0.01611641,0.0005679379,0.0008962076,0.001360781,0.0003252195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00152244,0.005505382,0.1952462,0.0005317737,0.0002159327,0.006608803,0.002762192,0.4882879,0.007497637,0.00723068,0.004614705,0.2799763],"study_design_scores_gemma":[0.0001377209,0.001021958,0.0163338,0.00003866113,0.00006465006,0.0007295179,0.000779648,0.9713164,0.005386867,0.002335636,0.001809239,0.00004580845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9515102,0.0002682753,0.04324468,0.0008253868,0.00002324799,0.0002040468,0.0002887893,0.0005231005,0.003112421],"genre_scores_gemma":[0.9482369,0.0001863278,0.05017878,0.00004045505,0.00001073792,0.00009457621,0.000185964,0.00003781146,0.001028552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01324624,"threshold_uncertainty_score":0.02633828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03033593223569978,"score_gpt":0.272739207723545,"score_spread":0.2424032754878452,"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."}}