{"id":"W2993429484","doi":"10.1109/icsme.2019.00031","title":"Aiding Code Change Understanding with Semantic Change Impact Analysis","year":2019,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Change impact analysis; Computer science; Semantic change; Context (archaeology); JavaScript; Unix; Code (set theory); False positive paradox; Source code; Software engineering; Programming language; Information retrieval; Data science; Artificial intelligence; Software","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":[0.0003448073,0.0001475821,0.0002278833,0.0006436931,0.00006734716,0.0002099081,0.00058981,0.00004264756,0.0001419089],"category_scores_gemma":[0.00003131273,0.0001066535,0.00009833771,0.002514409,0.0000166491,0.0007678738,0.0001993611,0.0001330501,0.0001385902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003035442,"about_ca_system_score_gemma":0.00002670561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003587511,"about_ca_topic_score_gemma":0.0001007593,"domain_scores_codex":[0.9985394,0.00002723005,0.00010087,0.0003884033,0.0004679166,0.0004762014],"domain_scores_gemma":[0.9988142,0.0003180595,0.00003343463,0.0006552286,0.00004187163,0.0001372645],"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":[0.000008343151,0.00002875964,0.9850678,0.00005528587,0.0005738403,0.0000445023,0.002108969,0.001113142,0.0001836229,0.009634781,0.00008352727,0.001097389],"study_design_scores_gemma":[0.0004857314,0.0003291668,0.3523014,0.00007505421,0.00007910318,0.00002195818,0.0001797225,0.6454328,0.0003391997,0.0002171071,0.00006692761,0.0004718293],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2500027,0.00006250568,0.7485433,0.0003391935,0.00008768655,0.0002720798,0.000001658774,0.0003902277,0.0003006927],"genre_scores_gemma":[0.9947858,0.000008859912,0.004821733,0.00008414136,0.0000566371,0.00002537771,0.000002393171,0.0000156707,0.0001993746],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7447831,"threshold_uncertainty_score":0.4349202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1191847257336823,"score_gpt":0.3164087000447,"score_spread":0.1972239743110176,"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."}}