{"id":"W2271850540","doi":"10.1145/2789209","title":"Test Case Prioritization Using Extended Digraphs","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Regression testing; Test case; Test suite; Digraph; Model-based testing; Machine learning; Prioritization; Data mining; Test (biology); Hidden Markov model; Fault detection and isolation; Artificial intelligence; Reliability engineering; Software; Regression analysis; Software development","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":[0.0007639822,0.0001782137,0.0002142166,0.0003061677,0.0001367799,0.00005865407,0.0002692669,0.0001335463,0.000001295068],"category_scores_gemma":[0.004487716,0.0001828028,0.00004830207,0.0004393942,0.00003785175,0.0002096418,0.00002376938,0.0002351933,0.000001765398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000452554,"about_ca_system_score_gemma":0.00005551233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009683441,"about_ca_topic_score_gemma":0.000002312408,"domain_scores_codex":[0.9989368,0.0001308564,0.0001957505,0.0003628648,0.0001190148,0.0002547112],"domain_scores_gemma":[0.9963054,0.002787988,0.00004630916,0.0005790939,0.000106296,0.0001748993],"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":[0.00003353971,0.0003372731,0.002589054,0.0001468864,0.000114827,0.001448411,0.003507898,0.05371286,0.001293676,0.002161092,0.0003763495,0.9342781],"study_design_scores_gemma":[0.00289408,0.002209126,0.002048404,0.000435475,0.0002654431,0.05759073,0.0001715614,0.8249974,0.01617119,0.08820202,0.00250334,0.002511259],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01071556,0.0002069989,0.9820781,0.00007110158,0.0004517626,0.00009826953,0.000004479808,0.006370334,0.000003349034],"genre_scores_gemma":[0.187448,0.00001409141,0.812385,0.00007509612,0.00002584597,0.0000169379,9.214228e-7,0.00001795322,0.00001625052],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9317669,"threshold_uncertainty_score":0.7454484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1583219119260009,"score_gpt":0.3445707515609084,"score_spread":0.1862488396349075,"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."}}