{"id":"W1836516978","doi":"10.1007/978-3-642-11928-6_19","title":"Concept Analysis as a Framework for Mining Functional Features from Legacy Code","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Programming language; Implementation; Set (abstract data type); Code (set theory); Class (philosophy); Functional programming; Inheritance (genetic algorithm); Software engineering; Theoretical computer science; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008062515,0.0006523614,0.0008967847,0.001063448,0.0003519858,0.0007540919,0.003258985,0.000786012,0.00004213662],"category_scores_gemma":[0.002414198,0.0006151075,0.000377801,0.001107246,0.0007635979,0.000786262,0.001039719,0.001577651,0.00001050005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002020335,"about_ca_system_score_gemma":0.0004490378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002579141,"about_ca_topic_score_gemma":0.00008606017,"domain_scores_codex":[0.9957803,0.00004573039,0.0005041506,0.002034896,0.0009136309,0.0007213037],"domain_scores_gemma":[0.9883068,0.009074253,0.0003645624,0.001735605,0.0003299559,0.0001888631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001771003,0.00001527992,0.00003568429,0.00001380555,0.0001730832,0.00003710946,0.00130891,0.6468548,0.0002488179,0.05904779,0.00002212554,0.2922249],"study_design_scores_gemma":[0.0002341062,0.0001272222,0.0004854032,0.0001701999,0.00009711352,0.00002221759,7.961933e-7,0.08571954,0.003418626,0.9074306,0.001406329,0.0008878528],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00007646043,0.0007986753,0.9930646,0.0005684716,0.004490359,0.0003526459,0.00004336578,0.0004919972,0.0001133663],"genre_scores_gemma":[0.009100568,0.00001205828,0.9884506,0.001101553,0.0009925996,0.00002876232,0.00003439254,0.00004368053,0.0002357862],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8483828,"threshold_uncertainty_score":0.99963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03597824644516302,"score_gpt":0.3035847591646655,"score_spread":0.2676065127195025,"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."}}