{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00251696,0.00115879,0.0008441789,0.007567551,0.0009186754,0.002792019,0.002435399,0.0008693885,0.002774791],"category_scores_gemma":[0.006638874,0.0005884015,0.002202547,0.005086121,0.00126363,0.002878885,0.001515637,0.001771569,0.001160707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009824422,"about_ca_system_score_gemma":0.002488709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005225611,"about_ca_topic_score_gemma":0.006197259,"domain_scores_codex":[0.9980914,0.0003732963,0.0001792985,0.0004084594,0.0008629349,0.00008451212],"domain_scores_gemma":[0.9958948,0.002770604,0.0003232048,0.0003564909,0.0005500416,0.0001049628],"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":[0.0001609145,0.0002542123,0.008292947,0.00113902,0.000283455,0.0006047341,0.001427318,0.02083524,0.01291836,0.1097743,0.007677772,0.8366317],"study_design_scores_gemma":[0.00006293254,0.0002558339,0.008066832,0.0005138681,0.0003860115,0.001701501,0.001018603,0.6250788,0.02094903,0.2883962,0.05343007,0.000140315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00646139,0.0005037927,0.989038,0.0001332092,0.00001650482,0.0001708089,0.000708448,0.00204184,0.0009259279],"genre_scores_gemma":[0.04161192,0.0002508727,0.9552625,0.00005423009,0.00001883653,0.0002455893,0.001644522,0.0001175196,0.0007940468],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007567551,"threshold_uncertainty_score":0.01331109,"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."}}