{"id":"W4284670745","doi":"10.1145/3510003.3510115","title":"Inferring and applying type changes","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 44th International Conference on Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Colorado Boulder; National Science Foundation","keywords":"Code refactoring; Computer science; Plug-in; Programming language; Maintainability; Type (biology); Precision and recall; Data type; Software engineering; Information retrieval; Data mining; Artificial intelligence; Software","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008309321,0.002183361,0.001580775,0.007119413,0.001424571,0.003727702,0.003083477,0.002167422,0.0027903],"category_scores_gemma":[0.07982732,0.001778083,0.002745374,0.003061022,0.001362488,0.003858324,0.003251539,0.003722255,0.002556623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001383905,"about_ca_system_score_gemma":0.004427241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01008988,"about_ca_topic_score_gemma":0.01687625,"domain_scores_codex":[0.980243,0.003322518,0.001897533,0.005345596,0.00833675,0.0008546785],"domain_scores_gemma":[0.9493616,0.02540774,0.003925497,0.01165262,0.009105073,0.0005475162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004611998,0.0003979106,0.1216882,0.002044215,0.0005081052,0.002383844,0.00292881,0.02456008,0.03187517,0.01195122,0.04369221,0.7575091],"study_design_scores_gemma":[0.0002049811,0.0003875753,0.04722558,0.001131754,0.001144743,0.004106015,0.001948954,0.526633,0.1456343,0.04494165,0.2261453,0.0004962073],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1187553,0.001771822,0.7781532,0.001356451,0.0008906879,0.001043806,0.01154852,0.0778802,0.008599901],"genre_scores_gemma":[0.2520061,0.001413926,0.7063318,0.001084797,0.0002991211,0.0004163557,0.02382269,0.00884079,0.005784517],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01008988,"threshold_uncertainty_score":0.04394442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03354994806902248,"score_gpt":0.2610165799266521,"score_spread":0.2274666318576296,"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."}}