{"id":"W4413216650","doi":"10.1145/3712255.3726730","title":"Learning to Predict Code Review Rounds in Modern Code Review Using Multi-Objective Genetic Programming","year":2025,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Concordia University","funders":"","keywords":"Computer science; Genetic programming; Programming language; Code (set theory); Code review; Artificial intelligence; Static program analysis; Software development; Software","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":[0.0005057466,0.0002056775,0.0003702222,0.0002402707,0.0001892116,0.00008652762,0.0006986953,0.00005745609,0.000001699862],"category_scores_gemma":[0.0006413226,0.000188084,0.00006257587,0.00129437,0.00008307892,0.0002304959,0.0006239206,0.0002852191,0.000001594917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001949836,"about_ca_system_score_gemma":0.0002437402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003984317,"about_ca_topic_score_gemma":0.000003919108,"domain_scores_codex":[0.9981289,0.00006951375,0.0005315084,0.0005239263,0.0004300411,0.0003161208],"domain_scores_gemma":[0.99863,0.0001757626,0.0001936742,0.0001572091,0.0007557507,0.00008754039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003354786,0.0003762493,0.4633175,0.01916848,0.0001160932,0.000002994514,0.00249916,0.1803961,0.002610823,0.001735721,0.001254677,0.3284886],"study_design_scores_gemma":[0.0002249676,0.00006259431,0.3147165,0.01092629,0.00002458967,0.00002210957,0.00004722245,0.6726902,0.00003566577,0.0009456344,0.000155831,0.0001484745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2318171,0.0349868,0.7302104,0.001137072,0.0001687393,0.001509385,0.000002788247,0.0001306046,0.00003712973],"genre_scores_gemma":[0.8608575,0.004446441,0.1343596,0.0001814284,0.00001630054,0.00007954465,0.000002131303,0.00001110723,0.00004598518],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6290404,"threshold_uncertainty_score":0.7669843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03497932190879448,"score_gpt":0.3047175831055146,"score_spread":0.2697382611967201,"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."}}