{"id":"W3213620637","doi":"10.1109/models50736.2021.00011","title":"Automated Patch Generation for Fixing Semantic Errors in ATL Transformation Rules","year":2021,"lang":"en","type":"article","venue":"","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Dependency (UML); Transformation (genetics); Model transformation; Semantics (computer science); Program transformation; Artificial intelligence; Programming language; Data mining; Template; Natural language processing; Theoretical computer science; Algorithm","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":[0.002165215,0.001297501,0.001016983,0.001219081,0.0004373951,0.001135847,0.001319616,0.001222188,0.002685817],"category_scores_gemma":[0.01088656,0.0006584639,0.001165424,0.0004324967,0.001095989,0.00112214,0.001946916,0.001164669,0.0007276245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005115541,"about_ca_system_score_gemma":0.001033413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001737621,"about_ca_topic_score_gemma":0.002035599,"domain_scores_codex":[0.9974706,0.0006906993,0.0002292475,0.0005932752,0.0008179685,0.0001982413],"domain_scores_gemma":[0.9944797,0.002723797,0.0005016106,0.001534964,0.0006372373,0.0001227325],"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":[0.0005168509,0.0005659569,0.01238087,0.0006884024,0.0001534783,0.001262551,0.0009413308,0.3126611,0.1188826,0.01185237,0.006441414,0.5336531],"study_design_scores_gemma":[0.00008301626,0.000196925,0.001444955,0.00004961923,0.00008616779,0.0003876854,0.0001365518,0.9166342,0.06841362,0.006408435,0.00612572,0.00003311417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0548823,0.000122565,0.9343375,0.00009192712,0.00003370467,0.0003098043,0.0001613824,0.008698764,0.001362],"genre_scores_gemma":[0.3294814,0.000110916,0.6659316,0.00008880521,0.00001473358,0.0002140625,0.0008599395,0.002199574,0.001099049],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002685817,"threshold_uncertainty_score":0.01145089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02450707322724635,"score_gpt":0.2656837688717137,"score_spread":0.2411766956444674,"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."}}