{"id":"W4292291355","doi":"10.1007/s10664-022-10193-8","title":"Revisiting the debate: Are code metrics useful for measuring maintenance effort?","year":2022,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Science Foundation of Sri Lanka; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Software maintenance; Code (set theory); Context (archaeology); Source code; Java; Software metric; Code review; Granularity; Software engineering; Data science; Software; Data mining; Software quality; Software development; Programming language; Set (abstract data type)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.08336804,0.001614027,0.002904942,0.00812288,0.002939994,0.01233404,0.007485365,0.01464219,0.007251221],"category_scores_gemma":[0.4054754,0.0007430993,0.001116417,0.00910185,0.02100223,0.030441,0.004137713,0.0185267,0.003474131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005062594,"about_ca_system_score_gemma":0.008644125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009260993,"about_ca_topic_score_gemma":0.008833054,"domain_scores_codex":[0.9389174,0.03452466,0.003801583,0.007523403,0.01384871,0.001384268],"domain_scores_gemma":[0.353723,0.5248331,0.02134246,0.01637542,0.07991143,0.003814475],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009903355,0.0004655627,0.02810573,0.003816533,0.0009802658,0.0001806574,0.006942483,0.001085831,0.0009400254,0.3139481,0.1806637,0.4618807],"study_design_scores_gemma":[0.0004058098,0.0005068498,0.04932445,0.01963708,0.0009408849,0.000577128,0.02187354,0.006946336,0.00202442,0.6485322,0.2488498,0.0003814751],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01697486,0.07811363,0.007493917,0.8806775,0.006486571,0.00002590567,0.0003192628,0.00006223463,0.009846051],"genre_scores_gemma":[0.6161676,0.07352123,0.01372697,0.2558015,0.03554881,0.0001665923,0.0006752111,0.0004126534,0.003979485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9166319,"threshold_uncertainty_score":0.4408976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05036001561640007,"score_gpt":0.2812554912041638,"score_spread":0.2308954755877637,"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."}}