{"id":"W4237865316","doi":"10.1109/icse.2015.238","title":"Mining Temporal Properties of Data Invariants","year":2015,"lang":"en","type":"article","venue":"2015 IEEE/ACM 37th IEEE International Conference on Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Correctness; Temporal logic; Computer science; Property (philosophy); Linear temporal logic; Field (mathematics); Theoretical computer science; Value (mathematics); Data mining; Programming language; Mathematics; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002381469,0.0007963679,0.0005845025,0.004094017,0.000628989,0.001308043,0.001222264,0.000611093,0.000894634],"category_scores_gemma":[0.0233749,0.0006932486,0.001484896,0.00228281,0.001268116,0.002995796,0.001316696,0.001379873,0.0003443561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001363985,"about_ca_system_score_gemma":0.003090587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005731779,"about_ca_topic_score_gemma":0.009716328,"domain_scores_codex":[0.9962547,0.0004436389,0.0004034004,0.0008381171,0.001780129,0.0002799578],"domain_scores_gemma":[0.9763693,0.01126503,0.004498394,0.004037541,0.003477358,0.0003522854],"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":[0.0007297933,0.000650435,0.2106468,0.001781917,0.0003663896,0.002459116,0.002300398,0.1480099,0.06926886,0.06667488,0.008441475,0.4886701],"study_design_scores_gemma":[0.00006055509,0.0002647853,0.01580173,0.000130794,0.0001494584,0.0006464787,0.0005056066,0.833238,0.07472587,0.06374277,0.01065608,0.00007791824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2766862,0.000538461,0.7060137,0.0005689088,0.00005157621,0.0003366084,0.005269606,0.008044349,0.00249054],"genre_scores_gemma":[0.7016611,0.0002394371,0.2884126,0.0001441579,0.0000323843,0.000306976,0.007496445,0.0005741853,0.001132732],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005731779,"threshold_uncertainty_score":0.01259458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2543589862944931,"score_gpt":0.3511994581912674,"score_spread":0.0968404718967743,"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."}}