{"id":"W2145544369","doi":"10.1109/icse.2007.90","title":"Tracking Code Clones in Evolving Software","year":2007,"lang":"en","type":"article","venue":"Proceedings/Proceedings - International Conference on Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":216,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"clone (Java method); Code refactoring; Computer science; Software; Software maintenance; Cloning (programming); Code (set theory); Source code; Tracking (education); Software system; Software development; Software evolution; Programming language; Software engineering; Software construction; Biology; Genetics; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001903573,0.0007131615,0.000547892,0.001968315,0.0001771545,0.001294332,0.002968912,0.0003375895,0.00008861172],"category_scores_gemma":[0.008782655,0.0008021892,0.0001774903,0.001696972,0.00006914603,0.002925847,0.0006833596,0.001396019,0.00008764158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008811069,"about_ca_system_score_gemma":0.0001221779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002577614,"about_ca_topic_score_gemma":0.000006510112,"domain_scores_codex":[0.9944102,0.000003980961,0.0009703556,0.001387157,0.001767562,0.001460804],"domain_scores_gemma":[0.9966711,0.0006720907,0.0002592428,0.000277916,0.001681455,0.0004382059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001568258,0.0004299416,0.6555899,0.0006492364,0.0002236332,0.0002344956,0.004918615,0.001601503,0.02172922,0.2872025,0.001613265,0.02565079],"study_design_scores_gemma":[0.00421705,0.0007468566,0.6801223,0.004773065,0.00003970648,0.0006530456,0.001456666,0.234303,0.055489,0.004731231,0.008336251,0.00513185],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5174297,0.0002056281,0.4712519,0.0006174673,0.002382277,0.0007068011,0.00001636982,0.004172546,0.003217311],"genre_scores_gemma":[0.8739387,0.00003977051,0.1250498,0.0001254866,0.0004545514,0.0000942547,0.000007948342,0.0001115978,0.0001779521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3565089,"threshold_uncertainty_score":0.9997424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0352682557164846,"score_gpt":0.2914512111336716,"score_spread":0.2561829554171869,"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."}}