{"id":"W2899100292","doi":"10.1145/3236024.3264599","title":"WarningsGuru: integrating statistical bug models with static analysis to provide timely and specific bug warnings","year":2018,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Commit; Computer science; False positive paradox; Static program analysis; Process (computing); Static analysis; Software bug; Code (set theory); Statistical model; Source code; Statistical analysis; Software; Debugging; Software engineering; Data science; Data mining; Database; Software development; Programming language; Set (abstract data type); Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005609508,0.0002188152,0.0003020133,0.0005007213,0.0001565818,0.0004785067,0.0006009993,0.00004247364,0.00009032134],"category_scores_gemma":[0.0005574752,0.0001634401,0.00003136884,0.001820294,0.0001346772,0.0005144808,0.0003481899,0.0002623767,0.0001073766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008525236,"about_ca_system_score_gemma":0.00009202011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000240577,"about_ca_topic_score_gemma":0.00006054835,"domain_scores_codex":[0.9977629,0.00006608904,0.0002657805,0.000726432,0.0006330137,0.0005457852],"domain_scores_gemma":[0.9976762,0.001058489,0.00005190268,0.0005523328,0.0003050415,0.0003560444],"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":[0.0004685648,0.0005263505,0.1636628,0.0003834114,0.003270081,0.0006556679,0.1177915,0.07255324,0.005617971,0.2318999,0.03708015,0.3660903],"study_design_scores_gemma":[0.000225349,0.0005963634,0.01260472,0.00003393936,0.00003883258,0.00001579164,0.0001269452,0.9830894,0.0005433391,0.001195512,0.001195685,0.000334061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1642916,0.00001633685,0.8343412,0.000388471,0.00003001214,0.0002046242,0.000003282377,0.0003456717,0.0003787298],"genre_scores_gemma":[0.5740796,0.000001511902,0.4252288,0.00008451226,0.00003183923,0.00001968064,0.00000281121,0.00001469363,0.0005365704],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9105362,"threshold_uncertainty_score":0.6664893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01718653479600835,"score_gpt":0.2631313732810692,"score_spread":0.2459448384850609,"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."}}