{"id":"W2767550398","doi":"10.1109/ase.2017.8115667","title":"Detecting unknown inconsistencies in web applications","year":2017,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Intel Corporation","keywords":"Computer science; JavaScript; Web application; Programming language; Code (set theory); Source code; Novelty; Matching (statistics); Set (abstract data type); Data mining; Information retrieval; World Wide Web","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":[0.0002808814,0.00004750536,0.00005723675,0.000098484,0.0001994116,0.0002780651,0.001183743,0.00002553358,0.000006296819],"category_scores_gemma":[0.0007114718,0.00004454103,0.00001660463,0.0001328952,0.00003539334,0.0003070633,0.0004573106,0.0001089783,0.00007612024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003663486,"about_ca_system_score_gemma":0.00006014663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006442232,"about_ca_topic_score_gemma":0.0003015486,"domain_scores_codex":[0.9994189,0.000009265586,0.00008394953,0.0001811174,0.0001302317,0.0001765709],"domain_scores_gemma":[0.9986948,0.0003074631,0.00002382939,0.0008916313,0.00003907064,0.00004321389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000001513703,0.00007811823,0.3699102,0.00004978156,0.00001447254,0.00003389852,0.0006747546,0.0004431915,0.007187779,0.1423845,0.0003732516,0.4788485],"study_design_scores_gemma":[0.0007288027,0.00005010015,0.7349393,0.00006047495,0.000002033182,0.00003792251,0.00008294879,0.2056089,0.01380952,0.006439755,0.03766949,0.0005707408],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1788038,0.00007832854,0.802465,0.00130558,0.0001474572,0.0002709511,3.543819e-7,0.0005482952,0.01638016],"genre_scores_gemma":[0.9555618,0.000003180882,0.04382097,0.00001860437,0.00002341475,0.00006430144,6.392406e-8,0.000003618169,0.0005040555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.776758,"threshold_uncertainty_score":0.2681388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02861547933864964,"score_gpt":0.2936265408075027,"score_spread":0.265011061468853,"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."}}