{"id":"W4284697050","doi":"10.1145/3510003.3512765","title":"Bots for pull requests","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 44th International Conference on Software Engineering","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Universidade Tecnológica Federal do Paraná; National Science Foundation","keywords":"Computer science; Interface (matter); Human–computer interaction; Coding (social sciences); Participatory design; Information overload; Software; Interface design; User interface; World Wide Web; Engineering; Operating system","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":[0.0003253456,0.00012566,0.0001250228,0.0001207546,0.0001726821,0.0001252765,0.0014376,0.00002575766,0.00001882273],"category_scores_gemma":[0.0004197124,0.0001124111,0.00009992745,0.0001942417,0.00001725745,0.0002102835,0.0004765144,0.0002157647,0.000001449776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000110392,"about_ca_system_score_gemma":0.00004277174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004901575,"about_ca_topic_score_gemma":1.580378e-7,"domain_scores_codex":[0.9988778,0.000003676274,0.0002122486,0.0002720684,0.0004413948,0.0001928582],"domain_scores_gemma":[0.9992834,0.00006814517,0.000155973,0.0001490466,0.0003024486,0.00004092546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003447994,0.00007544269,0.001968663,0.00008872044,0.00006470824,0.000001187931,0.0008416495,0.01155411,0.01886592,0.9578701,0.002448616,0.00618644],"study_design_scores_gemma":[0.001148395,0.0004570385,0.004051159,0.0005150092,0.00002516054,0.0001313624,0.0003127176,0.8737159,0.07069215,0.01481913,0.03328233,0.0008496256],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5138515,0.00009349324,0.4654458,0.005291317,0.007066253,0.001073768,0.0000774127,0.001093436,0.006006995],"genre_scores_gemma":[0.9697351,0.000002100463,0.02941458,0.0001388288,0.00008276061,0.00009479893,0.000001688551,0.00001530983,0.0005147705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9430509,"threshold_uncertainty_score":0.4583991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0213261466337766,"score_gpt":0.2358149082008024,"score_spread":0.2144887615670258,"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."}}