{"id":"W2537446610","doi":"10.1145/3001867.3001874","title":"Towards predicting feature defects in software product lines","year":2016,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Software bug; Software quality assurance; Software product line; Software quality; Naive Bayes classifier; Machine learning; Classifier (UML); Artificial intelligence; Data mining; Quality assurance; Software; Context (archaeology); Software development; Support vector machine; 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":[0.003367349,0.001787396,0.0008040502,0.005537635,0.0004873431,0.001813281,0.001109465,0.001839877,0.0007488025],"category_scores_gemma":[0.01978412,0.0005610516,0.0008177835,0.00174338,0.000450741,0.002346934,0.0006578712,0.001358707,0.00100846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008648356,"about_ca_system_score_gemma":0.0008900702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01098838,"about_ca_topic_score_gemma":0.01048347,"domain_scores_codex":[0.9978114,0.0005805222,0.0001491526,0.0005447114,0.0007728019,0.0001414931],"domain_scores_gemma":[0.9801834,0.009997177,0.002659215,0.001415626,0.005382513,0.0003621526],"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":[0.0004618216,0.000881932,0.2806385,0.0003094798,0.0001988983,0.0003535651,0.0003887359,0.2907158,0.01735342,0.001335403,0.004625758,0.4027368],"study_design_scores_gemma":[0.00001521084,0.0001466227,0.01204609,0.0000308373,0.0000337559,0.0001102731,0.00007684901,0.978911,0.005986657,0.001984928,0.0006362945,0.00002146012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5615966,0.0009771909,0.4294337,0.0005464831,0.00006471646,0.0002100268,0.0009214579,0.004621309,0.001628464],"genre_scores_gemma":[0.7864333,0.0002418038,0.2104466,0.00009150579,0.00003688136,0.00006262963,0.0014847,0.0001538869,0.001048681],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01098838,"threshold_uncertainty_score":0.02184886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01581170763519573,"score_gpt":0.2604466243986399,"score_spread":0.2446349167634442,"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."}}