{"id":"W4409642872","doi":"10.5220/0013211700003928","title":"Recommender Systems Approaches for Software Defect Prediction: A Comparative Study","year":2025,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Recommender system; Software bug; Software; Software engineering; Machine learning; 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":[0.01506723,0.00107666,0.001570588,0.0059152,0.0009483706,0.002855093,0.002232392,0.002296243,0.002371104],"category_scores_gemma":[0.04098796,0.0004322211,0.001272736,0.00549347,0.0006711509,0.00460169,0.001065105,0.001596463,0.0008069609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00194801,"about_ca_system_score_gemma":0.001207426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02062104,"about_ca_topic_score_gemma":0.01883208,"domain_scores_codex":[0.9908852,0.005244314,0.0003817367,0.0007896306,0.002499646,0.0001995433],"domain_scores_gemma":[0.8784586,0.1087443,0.001916675,0.002961459,0.007138045,0.0007808214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002366236,0.002689962,0.1054938,0.002083416,0.002444546,0.0001427746,0.001625439,0.04509437,0.0008717309,0.008287846,0.004730771,0.8241691],"study_design_scores_gemma":[0.0006072426,0.007322999,0.1674655,0.001063661,0.002799907,0.0007266648,0.0034171,0.783819,0.002315354,0.01468552,0.01541591,0.0003610745],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7763411,0.07660898,0.1128218,0.004327418,0.0004461301,0.0004787564,0.0009241685,0.0008526616,0.02719902],"genre_scores_gemma":[0.9373993,0.01169111,0.04668919,0.0002554643,0.0002771698,0.00008405813,0.0005994238,0.00004555662,0.002958689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02062104,"threshold_uncertainty_score":0.07968408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1201600002253087,"score_gpt":0.3276400159924681,"score_spread":0.2074800157671594,"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."}}