{"id":"W2156479366","doi":"10.5555/2820668.2820675","title":"Recommending features and feature relationships from requirements documents for software product lines","year":2015,"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 Calgary","funders":"","keywords":"Computer science; Software product line; Software engineering; Feature model; Heuristics; Software; Categorization; Feature (linguistics); Product (mathematics); Software requirements specification; Software system; Software development; Data mining; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007010808,0.0001692899,0.0001773537,0.0000844856,0.0001488689,0.0001328626,0.000414753,0.00007890689,0.000001180046],"category_scores_gemma":[0.00532997,0.0001432618,0.00002991047,0.0001863822,0.00002319393,0.0009407955,0.0002725246,0.0001936622,0.00000321545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006327027,"about_ca_system_score_gemma":0.00002961595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001305104,"about_ca_topic_score_gemma":0.000007915775,"domain_scores_codex":[0.9988463,0.0001059942,0.0001539805,0.0004728655,0.000176971,0.0002438893],"domain_scores_gemma":[0.9981802,0.001054212,0.00007599904,0.0004505452,0.0001230175,0.0001160569],"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":[0.0001898106,0.000162223,0.06754244,0.0002054357,0.0003655873,0.00002329124,0.0079024,0.01719492,0.002625796,0.03081737,0.3533446,0.5196262],"study_design_scores_gemma":[0.003774368,0.0004215173,0.04936424,0.000280226,0.00007369163,0.00006539794,0.0006511649,0.003205534,0.02157415,0.8428386,0.07589232,0.001858833],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002860198,0.001092782,0.9916031,0.002281395,0.001033852,0.0003174625,0.000008796107,0.0007337099,0.00006867984],"genre_scores_gemma":[0.0110617,0.00002064737,0.9869984,0.0001355698,0.0001636969,0.00005178625,0.0000185171,0.00001631075,0.001533416],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8120212,"threshold_uncertainty_score":0.6380857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1561792397933961,"score_gpt":0.3516513144717821,"score_spread":0.195472074678386,"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."}}