{"id":"W2158236248","doi":"10.1109/apsec.2005.100","title":"Supporting predictive change impact analysis: a control call graph based technique","year":2005,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Change impact analysis; Computer science; Call graph; Java; Regression testing; Control flow; Control flow graph; Program analysis; Software; Graph; Set (abstract data type); Software maintenance; Control (management); Static analysis; Data mining; Software engineering; Software system; Artificial intelligence; Theoretical computer science; Programming language; Software construction","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":[0.0008620399,0.0001542673,0.0002354915,0.0007447673,0.0000556843,0.0001039735,0.0007187525,0.00007548722,0.00009098921],"category_scores_gemma":[0.0003068325,0.0001246468,0.0002392219,0.001928851,0.00002530308,0.0004498724,0.00009047804,0.0002072269,0.00002320771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001393548,"about_ca_system_score_gemma":0.00008893047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002188692,"about_ca_topic_score_gemma":0.0000335914,"domain_scores_codex":[0.9983558,0.00006448277,0.0002257491,0.0003833148,0.0004237525,0.000546913],"domain_scores_gemma":[0.9985155,0.0004686356,0.00006043398,0.0006145931,0.0001357475,0.0002050217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007778306,0.0005218139,0.8388725,0.0000595635,0.002277048,0.0001818154,0.001588816,0.1005448,0.004690977,0.001122808,0.004748417,0.04531366],"study_design_scores_gemma":[0.0003483503,0.000146763,0.1454291,0.000008692811,0.00004535177,0.000004934953,0.000002932523,0.8510751,0.002578386,0.00004093294,0.0001480217,0.0001714699],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00627242,0.00005429351,0.9914823,0.0005944794,0.00002363497,0.0004994432,0.000009667944,0.0009191902,0.0001445737],"genre_scores_gemma":[0.9449772,0.000001030893,0.05423885,0.0003007067,0.00008078703,0.0003297114,0.000003617563,0.00001074484,0.00005738438],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9387047,"threshold_uncertainty_score":0.5082947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01523830419380241,"score_gpt":0.3112018476531329,"score_spread":0.2959635434593305,"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."}}