{"id":"W2018360547","doi":"10.1145/1137639.1137646","title":"Discovering aspects in requirements with repertory grid","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Repertory grid; Computer science; Viewpoints; Identification (biology); Grid; Requirements analysis; Requirements engineering; Decomposition; Software engineering; Programming language; Software; Psychology","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":[0.0001851879,0.00009477347,0.0001043241,0.0000794322,0.00002326252,0.00003825576,0.0003331451,0.00002162182,0.000002527196],"category_scores_gemma":[0.0000721536,0.00007330793,0.00001381408,0.0002600113,0.00002037378,0.0005943844,0.0001311518,0.00007763078,0.000005607455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007840151,"about_ca_system_score_gemma":0.00002068847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001147366,"about_ca_topic_score_gemma":0.00009782855,"domain_scores_codex":[0.9992058,0.00002768842,0.0001251583,0.000267415,0.0001526609,0.0002213042],"domain_scores_gemma":[0.9994417,0.0001287957,0.00002799318,0.000368304,0.00001315164,0.00002002854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00001696906,0.000101125,0.01837717,0.00003920331,0.000012421,0.0003974248,0.0003206067,0.7924139,0.007696024,0.1675142,0.0004322242,0.01267879],"study_design_scores_gemma":[0.004151933,0.0008391521,0.5482123,0.0005203785,0.00001196583,0.0002182959,0.0002273758,0.03676528,0.1641842,0.2334015,0.008353962,0.003113653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03493893,0.00005844359,0.9598997,0.0000734768,0.0002226265,0.00006283955,1.522932e-7,0.0004411614,0.004302662],"genre_scores_gemma":[0.3430564,0.00000213401,0.6565571,0.00003128064,0.00004004371,0.000008545942,4.450508e-7,0.000006717743,0.0002973928],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7556486,"threshold_uncertainty_score":0.298941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02041653726920719,"score_gpt":0.2521347116522267,"score_spread":0.2317181743830195,"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."}}