{"id":"W2134502495","doi":"10.1109/ase.2009.23","title":"Using String Distances for Test Case Prioritisation","year":2009,"lang":"en","type":"preprint","venue":"","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Test suite; Computer science; String (physics); Property (philosophy); Code (set theory); Test case; Test (biology); Random testing; Code coverage; Algorithm; Data mining; Theoretical computer science; Machine learning; Programming language; Software; Mathematics","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":[0.006036113,0.001554923,0.001238257,0.007125573,0.0007066476,0.002355351,0.002113625,0.001287223,0.002779678],"category_scores_gemma":[0.04364204,0.000376998,0.0007723096,0.005738849,0.001353847,0.003536904,0.002171653,0.00136953,0.0007883628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001312254,"about_ca_system_score_gemma":0.001394039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001318178,"about_ca_topic_score_gemma":0.001136004,"domain_scores_codex":[0.9852055,0.005559688,0.001334536,0.001638672,0.005899352,0.0003623756],"domain_scores_gemma":[0.9657278,0.02159026,0.003772807,0.003785797,0.004391044,0.0007322613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00120183,0.0002724986,0.006014182,0.0005258548,0.0001954049,0.0003281988,0.0004146666,0.06989428,0.03609291,0.03228426,0.002501469,0.8502746],"study_design_scores_gemma":[0.0003954027,0.001818629,0.008203784,0.0001723303,0.0002381776,0.001501309,0.0003904817,0.7407545,0.1098447,0.1177062,0.01873286,0.0002416558],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05364674,0.00059169,0.939862,0.0002360589,0.00009161291,0.000269915,0.0002725245,0.002750134,0.00227933],"genre_scores_gemma":[0.3332811,0.0003017369,0.6635685,0.0001291654,0.00008037858,0.0002861053,0.0007419889,0.000433903,0.001177137],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007125573,"threshold_uncertainty_score":0.0319224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1170776203460329,"score_gpt":0.3653392665077053,"score_spread":0.2482616461616724,"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."}}