{"id":"W4232396147","doi":"10.1109/issre.2007.36","title":"Using Machine Learning to Support Debugging with Tarantula","year":2007,"lang":"en","type":"article","venue":"Proceedings/Proceedings - International Symposium on Software Reliability Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Debugging; Computer science; Statement (logic); Ranking (information retrieval); Test (biology); Software bug; Decision tree; Fault (geology); Machine learning; Fault tree analysis; Test case; Algorithmic program debugging; Artificial intelligence; Tree (set theory); Reliability engineering; Programming language; Software; 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":[0.003009828,0.001343077,0.0007811107,0.002487146,0.0005981526,0.001235089,0.001972626,0.001003876,0.00205204],"category_scores_gemma":[0.01881219,0.0006163286,0.0009139507,0.001131227,0.0005716978,0.002443747,0.000983657,0.001810542,0.0009714415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006602221,"about_ca_system_score_gemma":0.001329542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003552772,"about_ca_topic_score_gemma":0.005096454,"domain_scores_codex":[0.9981993,0.0007064538,0.0001512917,0.0003566837,0.0004691621,0.0001170223],"domain_scores_gemma":[0.9873368,0.008777455,0.001165962,0.001440422,0.001103242,0.0001762316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005426982,0.0005464484,0.01041673,0.0003803697,0.0001925175,0.0006253872,0.000531788,0.361555,0.01537797,0.01586551,0.005052204,0.5889134],"study_design_scores_gemma":[0.00002120316,0.00007451984,0.0004409619,0.0000265787,0.00002085235,0.00009577228,0.00001638213,0.9824724,0.007779044,0.00759689,0.001433169,0.00002223497],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02135805,0.00008204186,0.9644998,0.0002011517,0.0000231287,0.0000841892,0.0001078177,0.01287608,0.0007676535],"genre_scores_gemma":[0.1962313,0.00008212782,0.8022658,0.0001135254,0.00002215626,0.00008875842,0.0003180526,0.00029836,0.0005798699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003552772,"threshold_uncertainty_score":0.01591766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01438298275075504,"score_gpt":0.2633939116122934,"score_spread":0.2490109288615384,"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."}}