{"id":"W4225333936","doi":"10.5267/j.ijdns.2022.2.011","title":"Understanding and predicting bugs fixed by API-migrations","year":2022,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Software Engineering Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Software bug; Computer science; Context (archaeology); Security bug; Schedule; Software engineering; Software; Computer security; Programming language; Biology; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006937134,0.0009243314,0.0004714855,0.009506959,0.0005469783,0.002268736,0.0009611835,0.0009885281,0.0009172658],"category_scores_gemma":[0.09647092,0.0005111651,0.000610714,0.003423352,0.0006058476,0.003268386,0.001243896,0.0009328164,0.0003893593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000782922,"about_ca_system_score_gemma":0.001069424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01001791,"about_ca_topic_score_gemma":0.01391261,"domain_scores_codex":[0.9934941,0.001327912,0.001039748,0.001311832,0.002411928,0.0004145498],"domain_scores_gemma":[0.841075,0.08413737,0.05095912,0.005869919,0.01569579,0.002262748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009871125,0.0001230319,0.9368909,0.0002073526,0.0000592976,0.0004246841,0.002342726,0.001943577,0.001863288,0.0002083173,0.0007876101,0.05505049],"study_design_scores_gemma":[0.00001732734,0.00051221,0.9478179,0.0001821622,0.0001889114,0.001293787,0.003966641,0.0389986,0.003160408,0.0006671697,0.003119163,0.00007573818],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885095,0.0006091716,0.008472906,0.000186856,0.00002418545,0.0000881434,0.0006459038,0.0005012114,0.0009621812],"genre_scores_gemma":[0.9841002,0.0003446025,0.01349002,0.00003186422,0.0000206896,0.00005637076,0.001280194,0.00008852404,0.0005876372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01001791,"threshold_uncertainty_score":0.03668749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08412188676902072,"score_gpt":0.3226650559591267,"score_spread":0.238543169190106,"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."}}