{"id":"W2025893091","doi":"10.1109/icsm.2011.6080814","title":"Source code comprehension strategies and metrics to predict comprehension effort in software maintenance and evolution tasks - an empirical study with industry practitioners","year":2011,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Program comprehension; Computer science; Comprehension; Code refactoring; Software maintenance; Source code; Semantics (computer science); Software metric; Programming language; Task (project management); Static program analysis; Empirical research; Software development; Artificial intelligence; Natural language processing; Software quality; Software; Software system; Statistics; Engineering","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01534639,0.00063562,0.0004360708,0.002444168,0.0003279208,0.001354351,0.0007850051,0.001328711,0.0008977533],"category_scores_gemma":[0.1481633,0.0004404259,0.000394912,0.001321193,0.0008175694,0.002796418,0.001109161,0.001027361,0.000371025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005110279,"about_ca_system_score_gemma":0.0003890612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001260149,"about_ca_topic_score_gemma":0.001740625,"domain_scores_codex":[0.9888971,0.005925513,0.001096457,0.001457801,0.002262052,0.0003609137],"domain_scores_gemma":[0.7110645,0.2268112,0.03094544,0.0095014,0.01901756,0.002659947],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003425901,0.001332929,0.9560508,0.0001427439,0.0001408713,0.0001227783,0.008816316,0.001297635,0.003098136,0.0000984707,0.0002221801,0.02833468],"study_design_scores_gemma":[0.00005451904,0.002065583,0.9755725,0.00004152894,0.00006647447,0.0003323933,0.004049763,0.01450699,0.002442142,0.0002956747,0.0005298667,0.0000425209],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985267,0.00005346216,0.001084373,0.00002066334,0.000001219796,0.00003446079,0.00002568109,0.0000108555,0.0002425608],"genre_scores_gemma":[0.9978288,0.0000345955,0.00173995,0.00001563204,0.000003767608,0.00007476684,0.0001202086,0.00001077297,0.0001715152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9846536,"threshold_uncertainty_score":0.08116043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04577460277014427,"score_gpt":0.3027428329301828,"score_spread":0.2569682301600386,"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."}}