{"id":"W2161652852","doi":"10.1109/icsm.2004.1357829","title":"Developing a multi-objective decision approach to select source-code improving transformations","year":2004,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Maintainability; Source code; Process (computing); Dependency (UML); Code refactoring; Transformation (genetics); Software engineering; Program slicing; Programming language; Software quality; Quality (philosophy); Heuristic; Software; Software development; 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":[0.007153086,0.001680594,0.001827097,0.003432886,0.000889668,0.002430574,0.002403036,0.001768087,0.002989131],"category_scores_gemma":[0.00809182,0.001193534,0.001596031,0.002023488,0.001139988,0.001966466,0.001506935,0.002129522,0.0003506884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002821711,"about_ca_system_score_gemma":0.003545388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005014655,"about_ca_topic_score_gemma":0.006022517,"domain_scores_codex":[0.9961749,0.001878758,0.0002511601,0.0005789067,0.0008215166,0.0002946821],"domain_scores_gemma":[0.9927796,0.005094546,0.0005918019,0.0001685422,0.001127216,0.0002382044],"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":[0.00007263328,0.000192856,0.001082126,0.0002092304,0.0001439145,0.000103505,0.0002239147,0.9159652,0.002033708,0.01522248,0.0004747839,0.06427563],"study_design_scores_gemma":[0.00002060801,0.00005771991,0.000103353,0.00001213227,0.000025756,0.00001122579,0.00003655936,0.9934908,0.0007996511,0.005090212,0.0003409765,0.00001094395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01187652,0.00009415351,0.9862191,0.0001829644,0.000009987004,0.0001728732,0.000037612,0.0001090465,0.001297782],"genre_scores_gemma":[0.2324275,0.0001343739,0.7657388,0.00008839828,0.00002764481,0.0003979444,0.0001124977,0.00005847418,0.001014456],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007153086,"threshold_uncertainty_score":0.03782958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02884387800114981,"score_gpt":0.2808676425912748,"score_spread":0.252023764590125,"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."}}