{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004682386,0.000213194,0.0002395984,0.0005138949,0.0001243174,0.0002172674,0.0003463465,0.0001620254,0.000004614955],"category_scores_gemma":[0.0002103783,0.0001713696,0.00001183661,0.001165965,0.00009537201,0.001361019,0.0004261555,0.0005753656,0.000003055089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000114564,"about_ca_system_score_gemma":0.0001278634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006116859,"about_ca_topic_score_gemma":0.0002449046,"domain_scores_codex":[0.9980464,0.0001145768,0.0002417312,0.0006646183,0.0005426203,0.0003900559],"domain_scores_gemma":[0.9986223,0.0003492513,0.00005392425,0.0004925422,0.0001819749,0.0003000116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009228429,0.0003072875,0.9925839,0.00002391606,0.00001412576,0.00004500144,0.002125849,0.00272229,0.00006635059,0.0001767672,0.0001485396,0.001693681],"study_design_scores_gemma":[0.0007761162,0.001637827,0.9665819,0.00006099937,0.000006832845,0.00008003791,0.002805078,0.02760603,0.00004327262,0.0001137825,0.00005847014,0.000229661],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.613391,0.00002908685,0.3858264,0.00005001422,0.00003579766,0.0004086221,0.000001268138,0.0002381902,0.00001963181],"genre_scores_gemma":[0.9288513,0.000003217439,0.07098003,0.00007061232,0.00001198222,0.00003383674,0.000002522703,0.0000175951,0.00002888686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3154604,"threshold_uncertainty_score":0.6988248,"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."}}