{"id":"W2766557196","doi":"10.1109/scam.2017.26","title":"On the Relationships Between Stability and Bug-Proneness of Code Clones: An Empirical Study","year":2017,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Commit; Computer science; Code (set theory); Java; Programming language; Stability (learning theory); Software bug; Software maintenance; Perspective (graphical); Empirical research; Type (biology); Software; Software system; Biology; Artificial intelligence; Database; Mathematics; Machine learning; Statistics","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":[0.009037328,0.0004102886,0.0003925521,0.003653988,0.0005834231,0.001360552,0.000789761,0.0007860613,0.001483075],"category_scores_gemma":[0.09871946,0.0003403688,0.0005491328,0.003848672,0.001342272,0.002555408,0.0009474691,0.001315545,0.0003131352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006097299,"about_ca_system_score_gemma":0.0006089204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002863498,"about_ca_topic_score_gemma":0.002861267,"domain_scores_codex":[0.9921966,0.002642804,0.00105792,0.001330893,0.00232013,0.0004517499],"domain_scores_gemma":[0.5936787,0.3289044,0.04722963,0.008332435,0.01909339,0.002761477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007785062,0.0001232696,0.9918921,0.00005500597,0.00007664257,0.0001578618,0.000838573,0.0006011944,0.0003011925,0.0001076468,0.0001058826,0.005662787],"study_design_scores_gemma":[0.00000679201,0.0002340345,0.9903694,0.00002123021,0.00005500351,0.0004188213,0.001439345,0.006574395,0.0004517993,0.0001319658,0.0002817755,0.00001535002],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998724,0.0001534535,0.0006457008,0.00003630478,0.000001635338,0.0000157189,0.0001195177,0.00001464682,0.000288995],"genre_scores_gemma":[0.9991042,0.00006458838,0.0004794227,0.000009268846,0.000004940669,0.00001309278,0.0002141605,0.000007459484,0.000102892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009037328,"threshold_uncertainty_score":0.04779452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2120828051783236,"score_gpt":0.3893369986924202,"score_spread":0.1772541935140966,"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."}}