{"id":"W7113415863","doi":"","title":"Large Language Models for Code Generation and Program Comprehension: Exploring Capabilities, Context, and Developer Adaptation","year":2025,"lang":"en","type":"article","venue":"University Library (University of Saskatchewan)","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Program comprehension; Documentation; Adaptation (eye); Automatic summarization; Comprehension; Code (set theory); Software development; Java","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008288639,0.000837084,0.0004497395,0.001362737,0.0006979941,0.003915485,0.001171089,0.000871134,0.002346054],"category_scores_gemma":[0.07133682,0.0007173031,0.0008369528,0.000774054,0.00162798,0.007650166,0.002851392,0.00187064,0.0004964018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001553777,"about_ca_system_score_gemma":0.001765807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004457937,"about_ca_topic_score_gemma":0.005066249,"domain_scores_codex":[0.9918877,0.005849422,0.0002720571,0.001035946,0.0007918276,0.0001629244],"domain_scores_gemma":[0.9161932,0.07123291,0.004236423,0.005334437,0.002332602,0.0006704629],"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":[0.00127111,0.001434173,0.1795414,0.001697341,0.000398794,0.001095294,0.09742483,0.07291225,0.03402988,0.07090037,0.006452785,0.5328417],"study_design_scores_gemma":[0.0001859823,0.001011804,0.03990044,0.0006359971,0.0003267513,0.0008113983,0.01930229,0.8188896,0.0168288,0.07344095,0.02840892,0.000257025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6101594,0.0007831164,0.3765192,0.001467872,0.00003800786,0.0004553427,0.0004060171,0.003108311,0.007062753],"genre_scores_gemma":[0.877063,0.0002515267,0.1202256,0.0001520156,0.00001492702,0.0003739357,0.0006181265,0.0004541136,0.0008468254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008288639,"threshold_uncertainty_score":0.04383504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03107741514271305,"score_gpt":0.2123028565328652,"score_spread":0.1812254413901522,"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."}}