{"id":"W1671697208","doi":"10.7287/peerj.preprints.1138v2","title":"The charming code that error messages are talking about","year":2015,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Python (programming language); Cyclomatic complexity; Debugging; Computer science; Programming language; Random testing; Code coverage; Source lines of code; Software quality; Software bug; Software; Syntax error; Source code; Code (set theory); Charm (quantum number); Software metric; Abstract syntax tree; Algorithm; Test case; Software development; Particle physics; Machine learning","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":[0.002614161,0.001576999,0.000588625,0.002487068,0.002059569,0.002858896,0.000929551,0.00221151,0.01350369],"category_scores_gemma":[0.04648969,0.0005537898,0.0004827727,0.002005445,0.003454528,0.005330741,0.002759717,0.003051101,0.006618674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001206059,"about_ca_system_score_gemma":0.001637693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002191891,"about_ca_topic_score_gemma":0.001868445,"domain_scores_codex":[0.991686,0.002464841,0.0005920579,0.0008665065,0.003925765,0.000464806],"domain_scores_gemma":[0.950754,0.01982016,0.01039236,0.007806764,0.010175,0.001051796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001562652,0.0003344461,0.05840784,0.002926858,0.0002743614,0.004067401,0.01627849,0.004371342,0.04795197,0.1461075,0.2112415,0.5064757],"study_design_scores_gemma":[0.00007202816,0.0004019355,0.02924158,0.002215197,0.0002270288,0.006442362,0.003582454,0.01063185,0.06547484,0.05838846,0.8229501,0.0003721258],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2970965,0.006111531,0.4163497,0.02751639,0.009213745,0.001231498,0.006745044,0.0630041,0.1727315],"genre_scores_gemma":[0.667053,0.002284665,0.1800126,0.01249216,0.001599872,0.0007260799,0.003741282,0.01619086,0.1158996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01350369,"threshold_uncertainty_score":0.04517436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07207317227592544,"score_gpt":0.3135958871748,"score_spread":0.2415227148988746,"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."}}