{"id":"W4213153767","doi":"10.4018/978-1-59140-255-8.ch008","title":"Regression Test Selection for Database Applications","year":2004,"lang":"en","type":"book-chapter","venue":"Advances in database research (ADR) book series/Advances in database research series","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Regression testing; Data mining; Control flow graph; Graph; Test suite; Test case; Regression analysis; Machine learning; Theoretical computer science; Programming language; Software; Software system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001614076,0.001006295,0.0006570632,0.002046136,0.000288675,0.001082993,0.001697174,0.0006291544,0.009324097],"category_scores_gemma":[0.008123822,0.0004074919,0.0006578894,0.001769457,0.0003030416,0.001058105,0.0005288147,0.001161772,0.002583305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006165001,"about_ca_system_score_gemma":0.0006084284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009448482,"about_ca_topic_score_gemma":0.00107591,"domain_scores_codex":[0.9979998,0.000624114,0.000109981,0.000271527,0.000904504,0.00009005082],"domain_scores_gemma":[0.9933619,0.004635742,0.0002837754,0.0004188348,0.001200673,0.00009914424],"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":[0.0001929735,0.0002005017,0.001692334,0.0004526549,0.00004151096,0.000523579,0.0001557811,0.01471285,0.01609398,0.01470463,0.02321661,0.9280125],"study_design_scores_gemma":[0.0002945888,0.001404973,0.009985995,0.0007721331,0.0002382297,0.004023938,0.000362571,0.6057882,0.1217932,0.07536346,0.1798454,0.0001273053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0491119,0.008628819,0.8640102,0.00125061,0.000371534,0.0007144229,0.0006518525,0.01543202,0.05982867],"genre_scores_gemma":[0.3004185,0.004783479,0.6400912,0.0007904195,0.0003472677,0.0008094123,0.002919482,0.002220563,0.04761966],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009324097,"threshold_uncertainty_score":0.03119224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05470746594474753,"score_gpt":0.4075092612454673,"score_spread":0.3528017953007198,"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."}}