{"id":"W2747474147","doi":"","title":"A Refactoring Technique for Large Groups of Software Clones","year":2017,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Concordia University","keywords":"Code refactoring; clone (Java method); Software maintenance; Maintainability; Software evolution; Cloning (programming); Computer science; Software; Code (set theory); Programming language; Software system; Software engineering; Biology; Genetics; Set (abstract data type); Software construction; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002028451,0.001430055,0.001051574,0.003897862,0.001112533,0.0008064232,0.002161857,0.001501548,0.001867482],"category_scores_gemma":[0.008985654,0.0007361395,0.001925403,0.002409599,0.0008517202,0.001859603,0.001666016,0.001494686,0.001251255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006119862,"about_ca_system_score_gemma":0.001540181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004255371,"about_ca_topic_score_gemma":0.005577568,"domain_scores_codex":[0.9967318,0.0003941379,0.0002805702,0.001129402,0.001294203,0.0001700031],"domain_scores_gemma":[0.9909893,0.002180028,0.001620914,0.002729343,0.002260595,0.000219879],"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":[0.0002131088,0.0001949613,0.01080755,0.0004468011,0.0001351656,0.001029879,0.001612146,0.008247486,0.1275233,0.00272214,0.005102615,0.8419649],"study_design_scores_gemma":[0.0002781727,0.001567757,0.02770702,0.000340854,0.0007746258,0.006099307,0.001161541,0.5110567,0.3478767,0.01479697,0.08805708,0.000283372],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08198287,0.000881132,0.8945296,0.0003101015,0.000102212,0.0004702594,0.0003796091,0.01968537,0.001658808],"genre_scores_gemma":[0.13589,0.0002299581,0.8576462,0.0001425791,0.00003395718,0.0001443149,0.001108313,0.001113739,0.003690933],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004255371,"threshold_uncertainty_score":0.01072758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02937693141409226,"score_gpt":0.3021023258651288,"score_spread":0.2727253944510366,"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."}}