{"id":"W2133853708","doi":"10.5555/776816.776844","title":"Data flow testing as model checking","year":2003,"lang":"en","type":"article","venue":"ScholarlyCommons (University of Pennsylvania)","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Model checking; Computer science; Computation tree logic; Temporal logic; Counterexample; Linear temporal logic; Construct (python library); CTL*; Programming language; Set (abstract data type); Test case; Algorithm; Theoretical computer science; Mathematics; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001730681,0.0001507917,0.0002160867,0.0002126022,0.0005057171,0.0001466122,0.003850273,0.0001117199,0.00003648196],"category_scores_gemma":[0.001137315,0.0002034374,0.0000576573,0.0009111727,0.0001145546,0.0051438,0.001190891,0.0004304664,0.0001147081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007131894,"about_ca_system_score_gemma":0.000231376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009646598,"about_ca_topic_score_gemma":0.00004707202,"domain_scores_codex":[0.9982786,0.0002583585,0.0001757117,0.000574807,0.0003998183,0.0003127494],"domain_scores_gemma":[0.9969517,0.0001346619,0.0002217445,0.002317434,0.0002293582,0.0001451026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003872223,0.0004311936,0.005823558,0.0001034207,0.0001135254,0.00009707484,0.003886997,0.01012098,0.02967411,0.8052521,0.001578714,0.1428796],"study_design_scores_gemma":[0.0003966645,0.00005500123,0.002043644,0.00003840483,0.00002483255,0.00003908762,0.0003829595,0.9803958,0.0006859524,0.01017486,0.005486843,0.0002759309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04505778,0.00006872429,0.9258991,0.0001765163,0.0001613574,0.0001409507,0.00002759134,0.0001800049,0.02828793],"genre_scores_gemma":[0.2443137,0.000007688559,0.7553065,0.00003920233,0.000007753059,1.516337e-7,0.000008778516,0.000008809129,0.0003074688],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9702748,"threshold_uncertainty_score":0.8295937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1557889296470551,"score_gpt":0.294810634822528,"score_spread":0.1390217051754729,"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."}}