{"id":"W2132992867","doi":"10.1109/icpc.2007.41","title":"Using Bayesian Belief Networks to Predict Change Propagation in Software Systems","year":2007,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Bayesian network; Program comprehension; Software evolution; Dependency (UML); Java; Probabilistic logic; Software system; Change impact analysis; Software maintenance; Software; Software development; Data mining; Software engineering; Machine learning; Artificial intelligence; Theoretical computer science; Programming language; Software construction","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":[0.004363472,0.001146837,0.0006333777,0.003785111,0.000487122,0.001310794,0.0009398427,0.00143759,0.0008862372],"category_scores_gemma":[0.03815195,0.001045622,0.0007092181,0.001563166,0.0005573789,0.003089454,0.0006587788,0.001444994,0.0002825524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001271137,"about_ca_system_score_gemma":0.0007701751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01685964,"about_ca_topic_score_gemma":0.01653928,"domain_scores_codex":[0.9983035,0.0007206673,0.0001107821,0.0002741058,0.0004759084,0.0001149989],"domain_scores_gemma":[0.9619621,0.0321659,0.002731367,0.0009587993,0.001883967,0.0002978912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003104447,0.000174977,0.06883892,0.00009593421,0.0001869121,0.0001182124,0.0002864684,0.8492609,0.002219974,0.002366035,0.0006118682,0.07552931],"study_design_scores_gemma":[0.000006802906,0.00001767762,0.003551962,0.000006965102,0.0000173957,0.00001676359,0.00001095814,0.9935192,0.0006134826,0.002143182,0.00008382169,0.00001180417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4736806,0.0004676745,0.5218315,0.0003211507,0.00002511623,0.0001081589,0.0005095602,0.00149297,0.001563276],"genre_scores_gemma":[0.9193847,0.0001931795,0.07927424,0.00004114802,0.00002020452,0.00007044031,0.0006026335,0.00006815886,0.000345275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01685964,"threshold_uncertainty_score":0.03352302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04411527325135882,"score_gpt":0.2925908469978418,"score_spread":0.248475573746483,"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."}}