{"id":"W2122525845","doi":"10.1109/icsm.2001.972708","title":"Supporting software maintenance by mining software update records","year":2002,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Software maintenance; Relevance (law); Context (archaeology); Software; Relation (database); Software engineering; Code (set theory); Software bug; Software system; Data mining; Programming language; Set (abstract data type)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00613989,0.0008866425,0.001146022,0.01037145,0.001330773,0.00282098,0.002329615,0.00117764,0.001208073],"category_scores_gemma":[0.03836486,0.0006407211,0.00113974,0.007495703,0.0006538049,0.004326781,0.001730384,0.001253326,0.00113445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006309385,"about_ca_system_score_gemma":0.001970348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003434203,"about_ca_topic_score_gemma":0.006673479,"domain_scores_codex":[0.9943985,0.001694138,0.0006207408,0.0008445553,0.002168271,0.000273772],"domain_scores_gemma":[0.953142,0.03268349,0.004913778,0.004547151,0.004306006,0.0004075535],"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":[0.0003635649,0.001208356,0.1478486,0.001941077,0.0003122252,0.0007217936,0.002943182,0.02894903,0.01701749,0.006735554,0.005674548,0.7862847],"study_design_scores_gemma":[0.0002318125,0.0008452389,0.1071744,0.001132566,0.0009913773,0.002536471,0.004648326,0.6652201,0.09645066,0.05809173,0.06237054,0.0003067287],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.33776,0.001733816,0.6375974,0.001610073,0.00008712627,0.001072973,0.008257636,0.00587905,0.006001914],"genre_scores_gemma":[0.4089279,0.0009840908,0.567803,0.0001274529,0.0001445371,0.000464317,0.02040608,0.0001807557,0.0009619409],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01037145,"threshold_uncertainty_score":0.03247124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01790051301451302,"score_gpt":0.2614285986194306,"score_spread":0.2435280856049176,"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."}}