{"id":"W2113476536","doi":"10.1109/icsm.2005.42","title":"Dynamic feature traces: finding features in unfamiliar code","year":2005,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Heuristics; Feature (linguistics); Relevance (law); Test suite; TRACE (psycholinguistics); Ranking (information retrieval); Code (set theory); Suite; Source code; Data mining; Binary code; Quality (philosophy); Artificial intelligence; Binary number; Machine learning; Information retrieval; Test case; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002838846,0.000125922,0.0001223674,0.0002577006,0.00004399401,0.0001281443,0.0009293686,0.0001030651,0.00002202906],"category_scores_gemma":[0.0002165745,0.0001108716,0.00003870123,0.0006633967,0.00001932796,0.0004046352,0.0001631589,0.0004014433,0.00008983295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001671264,"about_ca_system_score_gemma":0.00004842246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002593578,"about_ca_topic_score_gemma":0.0004420057,"domain_scores_codex":[0.9987827,0.00002764237,0.0001102876,0.0003497128,0.0003210024,0.000408631],"domain_scores_gemma":[0.9990398,0.0003798641,0.0000155549,0.0004546461,0.00002576506,0.00008438557],"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":[0.00004345979,0.0006198615,0.09152253,0.0002457652,0.0001003974,0.0005949843,0.009790196,0.1488626,0.01305225,0.06320028,0.1062938,0.5656739],"study_design_scores_gemma":[0.0008716383,0.00006850958,0.4786202,0.000095124,0.000002296687,0.0000818833,0.00008301433,0.4939339,0.003369057,0.0005585265,0.02173331,0.0005825576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4966927,0.002191096,0.4774609,0.01548615,0.000632466,0.000521618,0.000006695824,0.001919441,0.005088895],"genre_scores_gemma":[0.8558483,0.00002925314,0.1376065,0.0001851027,0.00003248506,0.00001264973,0.00000168895,0.00001272629,0.006271228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5650913,"threshold_uncertainty_score":0.4521214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01121693938005001,"score_gpt":0.2793472670697452,"score_spread":0.2681303276896951,"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."}}