{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005590556,0.001710736,0.001200943,0.01400349,0.001070843,0.00347105,0.001659521,0.001321397,0.00209673],"category_scores_gemma":[0.04494857,0.0008081968,0.001657952,0.005683126,0.001428976,0.005272109,0.002882751,0.002798685,0.0007951967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001627103,"about_ca_system_score_gemma":0.00250775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004054644,"about_ca_topic_score_gemma":0.006072884,"domain_scores_codex":[0.9906579,0.001987015,0.0009129905,0.001484312,0.004577827,0.0003799394],"domain_scores_gemma":[0.9430737,0.03130338,0.008278416,0.006593307,0.01033399,0.0004171891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006381585,0.0006100555,0.09713649,0.00159756,0.0003484357,0.001312274,0.006713248,0.02221175,0.04664514,0.02070503,0.01562584,0.7864561],"study_design_scores_gemma":[0.0001243413,0.0005207112,0.07036316,0.0005589981,0.0005464897,0.001752427,0.003313832,0.7118118,0.09808985,0.06829092,0.04425266,0.0003748389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1363921,0.0008737128,0.8197194,0.001383102,0.0001667342,0.0008975118,0.002396981,0.0320592,0.006111248],"genre_scores_gemma":[0.4551943,0.000409421,0.5372997,0.0002645937,0.0001112887,0.0003504579,0.003399863,0.001489893,0.001480482],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01400349,"threshold_uncertainty_score":0.02956605,"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."}}