{"id":"W1966968814","doi":"10.1109/icstw.2013.17","title":"A Call Graph Mining and Matching Based Defect Localization Technique","year":2013,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Debugging; Software bug; Program slicing; Static analysis; Java; Tree (set theory); Path (computing); Matching (statistics); Source code; Software; Focus (optics); Code (set theory); Call graph; Distributed computing; Theoretical computer science; Programming language; Set (abstract data type)","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":[0.0006077993,0.0008997044,0.0009249939,0.005484997,0.0007068352,0.0007631512,0.001884337,0.001401196,0.001850017],"category_scores_gemma":[0.003055379,0.0004417308,0.001394701,0.00306723,0.0004186372,0.001350822,0.0009785336,0.0008923907,0.00106088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005556655,"about_ca_system_score_gemma":0.001796095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006286398,"about_ca_topic_score_gemma":0.008172613,"domain_scores_codex":[0.9984069,0.0001252462,0.0001104546,0.0004086922,0.0008024325,0.0001462585],"domain_scores_gemma":[0.9980317,0.0004910153,0.0003901199,0.0003466164,0.0006645753,0.00007593158],"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":[0.0002498176,0.0004975944,0.01401382,0.0003653556,0.0001681886,0.001004187,0.0003383663,0.03009506,0.07820421,0.005819646,0.009062901,0.8601808],"study_design_scores_gemma":[0.00005629649,0.0003470431,0.0116508,0.0000585114,0.0001953537,0.002986416,0.0002914618,0.8909385,0.07126876,0.008139332,0.01397926,0.00008830259],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07504461,0.0005037618,0.9101663,0.0003620251,0.00006687531,0.0004027301,0.0007597597,0.01014571,0.002548257],"genre_scores_gemma":[0.362967,0.000285212,0.6283695,0.0002305895,0.00003664169,0.0002516478,0.001954826,0.0004189472,0.005485564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006286398,"threshold_uncertainty_score":0.01249963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009776589453432048,"score_gpt":0.2358316760854896,"score_spread":0.2260550866320576,"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."}}