{"id":"W4248354362","doi":"10.22215/etd/2018-13273","title":"Comparison of Approaches to Category Partition Specifications, Selection Criteria, and the Impact of the ‘Error’ and ‘Single’ Annotations using Industrial Case Studies","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Partition (number theory); Computer science; Selection (genetic algorithm); Code (set theory); Reliability engineering; Regression testing; Code coverage; Test (biology); White-box testing; Software; Data mining; Machine learning; Programming language; Engineering; Mathematics; Software system; Software construction","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":[0.02796528,0.001695808,0.001080937,0.006068761,0.001010711,0.003192301,0.002627068,0.00197004,0.001245251],"category_scores_gemma":[0.1000081,0.0007288081,0.001242477,0.004008621,0.001567512,0.003208156,0.002379929,0.001571494,0.000208239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003118225,"about_ca_system_score_gemma":0.00356449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006303095,"about_ca_topic_score_gemma":0.01167132,"domain_scores_codex":[0.9662105,0.02086668,0.002330673,0.001793195,0.00778398,0.001014953],"domain_scores_gemma":[0.7394629,0.232414,0.007328331,0.007148688,0.01190189,0.001744195],"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":[0.003681182,0.00290232,0.04150598,0.00312137,0.0008036304,0.0004974652,0.004396905,0.2885427,0.01784282,0.01773339,0.003002645,0.6159695],"study_design_scores_gemma":[0.0008142521,0.003962014,0.02892634,0.0008991769,0.001089072,0.0007063869,0.006665539,0.9039158,0.03224596,0.01289556,0.007596812,0.000283093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8391541,0.003209478,0.146584,0.0008397106,0.00006956545,0.0008913541,0.0003305989,0.001084732,0.007836588],"genre_scores_gemma":[0.7574703,0.0009452525,0.2390773,0.0001638586,0.00002338634,0.0004630733,0.0008313722,0.0002877682,0.00073761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02796528,"threshold_uncertainty_score":0.1478963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5374655900029118,"score_gpt":0.4350745023918684,"score_spread":0.1023910876110434,"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."}}