{"id":"W2760127551","doi":"10.1109/tse.2017.2757480","title":"On the Use of Hidden Markov Model to Predict the Time to Fix Bugs","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Hidden Markov model; Software bug; Software regression; Context (archaeology); Software; Markov model; Predictive modelling; Data mining; Software engineering; Software development; Markov chain; Data science; Software quality; Machine learning; Artificial intelligence; Programming language","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":[0.004174621,0.001321855,0.001047262,0.003339614,0.0007184788,0.001135379,0.001160471,0.001622854,0.001096136],"category_scores_gemma":[0.01126692,0.0006017321,0.001574513,0.001890081,0.0003205852,0.00205049,0.0005797233,0.001709861,0.0009012617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008307841,"about_ca_system_score_gemma":0.001470818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04175299,"about_ca_topic_score_gemma":0.03735791,"domain_scores_codex":[0.9989114,0.0004644015,0.00008749228,0.0002675962,0.0001705992,0.00009842088],"domain_scores_gemma":[0.9855887,0.01256292,0.0005554451,0.0003652179,0.0007701967,0.0001574578],"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":[0.0007758011,0.0009304576,0.06730606,0.0002096377,0.0005695799,0.0003432932,0.0004082154,0.5448481,0.003510285,0.003678328,0.004960295,0.3724599],"study_design_scores_gemma":[0.000007336182,0.00004301905,0.002042619,0.00001518969,0.00002756143,0.00003667335,0.0000190757,0.9962619,0.0003662425,0.001004842,0.0001620954,0.00001347502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2555553,0.002539608,0.7322895,0.001618627,0.0002287594,0.000159064,0.001115865,0.003887479,0.002605832],"genre_scores_gemma":[0.8704174,0.001345411,0.1232456,0.0003056995,0.0001789951,0.0001075305,0.001920993,0.000121922,0.002356571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04175299,"threshold_uncertainty_score":0.08301991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03385569789076816,"score_gpt":0.2492526278261282,"score_spread":0.21539692993536,"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."}}