{"id":"W2108761763","doi":"10.1109/ease.2009.11","title":"Change Support in Adaptive Software: A Case Study for Fine-Grained Adaptation","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adaptation (eye); Computer science; Granularity; Hierarchy; Context (archaeology); Software evolution; Set (abstract data type); Software engineering; Software system; Software; Adaptive system; Software construction; Artificial intelligence; Programming language","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":[0.002294737,0.0004305733,0.0004245803,0.000673859,0.001535336,0.001188758,0.001684524,0.002154584,0.0009893704],"category_scores_gemma":[0.009785544,0.0002758411,0.000474052,0.0008546127,0.001592436,0.001548302,0.00143578,0.001417471,0.000195816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001021473,"about_ca_system_score_gemma":0.0008020538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004965599,"about_ca_topic_score_gemma":0.007668429,"domain_scores_codex":[0.9974623,0.001338692,0.0001332939,0.0002738785,0.0005359893,0.0002558439],"domain_scores_gemma":[0.9888368,0.006917536,0.0006561266,0.002107659,0.0007841376,0.0006978303],"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":[0.00222843,0.01484757,0.1277124,0.001378204,0.0002972005,0.04765904,0.05649657,0.2432449,0.1131228,0.03653497,0.007710212,0.3487676],"study_design_scores_gemma":[0.000968297,0.007059343,0.09183127,0.0002367509,0.0002772485,0.01370801,0.03289722,0.6706293,0.0998678,0.01983622,0.06235216,0.0003363709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812948,0.00007522885,0.01502592,0.0002533698,0.00001314669,0.0002167001,0.00004128834,0.0002101355,0.002869484],"genre_scores_gemma":[0.9811322,0.00005391702,0.01760888,0.00006681927,0.000007333312,0.00007848097,0.00004893303,0.00004181562,0.0009615828],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004965599,"threshold_uncertainty_score":0.01213586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1636768445273394,"score_gpt":0.3508211035260436,"score_spread":0.1871442589987041,"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."}}