{"id":"W2591607085","doi":"","title":"An effective method for detecting duplicate crash reports using crash traces and hidden Markov models","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Crash; Computer science; Hidden Markov model; Software; Task (project management); Eclipse; Data mining; Machine learning; Artificial intelligence; Operating system; 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":[0.002524865,0.001482372,0.001623643,0.004678395,0.0008315928,0.001187897,0.002830558,0.001773436,0.0008473453],"category_scores_gemma":[0.01126812,0.0009355716,0.00125187,0.002138499,0.0005751005,0.002384052,0.001890945,0.001942627,0.0008224741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006453946,"about_ca_system_score_gemma":0.002170703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0063608,"about_ca_topic_score_gemma":0.006796144,"domain_scores_codex":[0.996879,0.0005863001,0.0003211443,0.0006292205,0.001353277,0.0002310794],"domain_scores_gemma":[0.9897174,0.004308424,0.001288878,0.001679187,0.002684429,0.0003217221],"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":[0.0005332478,0.0006575204,0.04386118,0.0005068206,0.0004659233,0.001448938,0.0007464152,0.06084267,0.03508417,0.00597674,0.00901154,0.8408648],"study_design_scores_gemma":[0.00004893354,0.0001364314,0.005889385,0.00004334403,0.0002033883,0.00125676,0.0001641522,0.957559,0.02433022,0.006114803,0.004134342,0.0001191618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02079778,0.0004132153,0.9744827,0.0001353673,0.00007066876,0.0001439652,0.000262962,0.003331613,0.0003617249],"genre_scores_gemma":[0.3738264,0.0004635926,0.6213073,0.0001719,0.0001382592,0.0002624129,0.001534952,0.0002822669,0.002012888],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0063608,"threshold_uncertainty_score":0.01335293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01780779090722437,"score_gpt":0.2837781660799824,"score_spread":0.265970375172758,"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."}}