{"id":"W2165129291","doi":"10.1109/csmr.2012.39","title":"Using fuzzy code search to link code fragments in discussions to source code","year":2012,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Traceability; Computer science; Source code; Code review; KPI-driven code analysis; Internal documentation; Software engineering; Documentation; Static program analysis; Requirements traceability; Code (set theory); Software maintenance; Fuzzy logic; Software evolution; Software; Software development; Information retrieval; Programming language; Software construction; Artificial intelligence","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":[0.004303666,0.0006702403,0.0005122201,0.01649822,0.001469286,0.002549494,0.001032595,0.0009522044,0.004841367],"category_scores_gemma":[0.03459017,0.0004016068,0.0005242437,0.007673481,0.001262764,0.004500999,0.002022716,0.0006478436,0.0008592035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00163154,"about_ca_system_score_gemma":0.001669188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009472483,"about_ca_topic_score_gemma":0.01056606,"domain_scores_codex":[0.9963602,0.001320796,0.0002731176,0.0005621443,0.001341934,0.0001417782],"domain_scores_gemma":[0.9616529,0.0301189,0.003125098,0.001937713,0.002736973,0.0004283603],"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":[0.001162264,0.0004362979,0.04008706,0.001519004,0.0001777323,0.0005679906,0.02140803,0.01267746,0.03952562,0.02772613,0.002681142,0.8520312],"study_design_scores_gemma":[0.0003604098,0.001625678,0.1373468,0.001344776,0.0005441403,0.002874212,0.0243495,0.5373721,0.1284753,0.1204752,0.04455719,0.0006747284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3626954,0.0007668427,0.6139182,0.000552871,0.00005689045,0.0008379294,0.0007296527,0.004039613,0.01640261],"genre_scores_gemma":[0.6259892,0.0003126914,0.3695543,0.00007971749,0.00001696542,0.0003455735,0.0006219359,0.0002251245,0.002854645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01649822,"threshold_uncertainty_score":0.02276021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07597034745385421,"score_gpt":0.3581996075529381,"score_spread":0.2822292600990838,"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."}}