{"id":"W3088218866","doi":"10.1109/tse.2020.3025732","title":"Automated Generation of Consistent Graph Models With Multiplicity Reasoning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Model-Driven Software Engineering Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Nemzeti Kutatási, Fejlesztési és Innovaciós Alap; Natural Sciences and Engineering Research Council of Canada; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; Innovációs és Technológiai Minisztérium; Emberi Eroforrások Minisztériuma","keywords":"Computer science; Solver; Theoretical computer science; Predicate abstraction; Graph; Abstraction; Programming language; Model checking","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009728057,0.0002731096,0.0002966741,0.0001974227,0.00007936661,0.00004849684,0.0003909454,0.0001056683,0.00000327263],"category_scores_gemma":[0.000007369164,0.0002751265,0.0001121595,0.0006402903,0.00002463507,0.0004977091,0.000005803778,0.000287524,0.00000276378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000679455,"about_ca_system_score_gemma":0.00004598199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002063028,"about_ca_topic_score_gemma":0.000002135184,"domain_scores_codex":[0.9986081,0.00002288348,0.000324555,0.0004381383,0.0003415045,0.0002648318],"domain_scores_gemma":[0.9990892,0.00006480909,0.00008149016,0.000455663,0.000139707,0.0001691739],"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":[0.000008734667,0.00003807912,0.00001297774,0.00005655478,0.00005384425,0.000008141177,0.0004340631,0.9888047,0.004291499,0.002436353,0.00003530225,0.003819763],"study_design_scores_gemma":[0.000271731,0.00015414,0.00003388918,0.0001017812,0.00001805091,0.00001428774,0.000001685455,0.9224831,0.07657055,0.000006422901,0.00008065463,0.0002637235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0122142,0.00003223089,0.9808787,0.00004726106,0.0001598459,0.0002651632,0.00001774684,0.006381446,0.000003447694],"genre_scores_gemma":[0.5005982,0.000006880347,0.4992746,0.00003629315,0.00001345785,0.00004175846,0.000001646616,0.00002508825,0.000002079493],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.488384,"threshold_uncertainty_score":0.9999701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03086150286293446,"score_gpt":0.2177657069829095,"score_spread":0.186904204119975,"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."}}