{"id":"W2241447802","doi":"10.1109/ase.2015.47","title":"Semantic Slicing of Software Version Histories (T)","year":2015,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Program slicing; Slicing; Set (abstract data type); Programming language; Correctness; Java; Software engineering; Software; Software maintenance; Feature (linguistics); Software system; 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":[0.0002394268,0.0000517311,0.00008392357,0.00009666473,0.00001999552,0.00001996668,0.0004620043,0.0000254077,0.000009641317],"category_scores_gemma":[0.000914068,0.00004620991,0.00002142071,0.000284253,0.0000205452,0.0002254062,0.0002328128,0.00005876513,0.00005369488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009215003,"about_ca_system_score_gemma":0.00008583276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009944913,"about_ca_topic_score_gemma":0.000002460957,"domain_scores_codex":[0.9992775,0.00001374309,0.0000899743,0.0001337607,0.0003428766,0.0001421374],"domain_scores_gemma":[0.9991649,0.0002368885,0.00001867403,0.000349213,0.0001434433,0.00008690071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005039806,0.0003126232,0.6181644,0.0006159066,0.000105933,0.000161709,0.02195666,0.01123176,0.00610507,0.06198982,0.2144834,0.06482238],"study_design_scores_gemma":[0.008135705,0.003234709,0.1791151,0.00069528,0.00005389146,0.0002596722,0.001846032,0.3001768,0.2575374,0.01713721,0.2282847,0.003523543],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08693919,0.0001118878,0.9114032,0.0001671011,0.0004268221,0.00004581495,2.458697e-7,0.0003653108,0.0005403968],"genre_scores_gemma":[0.8970188,0.00000105384,0.102085,0.00001744449,0.00001912823,0.000001465858,4.406682e-7,0.000005086442,0.0008515673],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8100796,"threshold_uncertainty_score":0.1884385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02676882948110706,"score_gpt":0.2551487347505602,"score_spread":0.2283799052694531,"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."}}