{"id":"W4313563640","doi":"10.1145/3551349.3556917","title":"Automatic Comment Generation via Multi-Pass Deliberation","year":2022,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China; Youth Innovation Promotion Association; Chinese Academy of Sciences; National Science Foundation","keywords":"Computer science; Deliberation; Code (set theory); Iterative and incremental development; Process (computing); Python (programming language); Java; Source lines of code; Programming language; Artificial intelligence; Software; Software 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":[0.0002293904,0.0000558086,0.00005474108,0.00004519823,0.0002717835,0.00008549567,0.0003089121,0.00001152442,0.0002198554],"category_scores_gemma":[0.000004281358,0.00005567479,0.00002174135,0.0001110264,0.000003125078,0.0002318222,0.0002692251,0.00006721016,0.00002440931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001072479,"about_ca_system_score_gemma":0.00002545305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004764683,"about_ca_topic_score_gemma":0.00001829638,"domain_scores_codex":[0.9992226,0.00008478792,0.0001653314,0.0001941699,0.0002246985,0.000108459],"domain_scores_gemma":[0.9995795,0.00001344208,0.00003880828,0.0003158205,0.00002236374,0.00003000356],"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":[7.801054e-7,0.000278589,0.0001854509,0.00001071659,0.00001945566,0.000009012593,0.002645326,0.149545,0.01703535,0.112264,0.01392758,0.7040788],"study_design_scores_gemma":[0.0001654246,0.00002369963,0.0001169444,4.971511e-7,0.000001314149,0.000006684834,0.00001496344,0.996057,0.0009087661,0.0003508058,0.002283111,0.00007072975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0193309,0.00001533549,0.9739591,0.005675385,0.0004283059,0.0001369025,3.187366e-7,0.000214204,0.0002395896],"genre_scores_gemma":[0.6358575,3.967559e-7,0.3603458,0.003400852,0.00003635656,0.00005687368,0.000006351612,0.000003193369,0.0002926903],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8465121,"threshold_uncertainty_score":0.2407262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04805307435243965,"score_gpt":0.2612893608342793,"score_spread":0.2132362864818396,"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."}}