{"id":"W2945530585","doi":"10.1007/s10664-019-09719-4","title":"Fostering good coding practices through individual feedback and gamification: an industrial case study","year":2019,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Coding (social sciences); Variety (cybernetics); Computer science; Best practice; Code review; Software; Quality (philosophy); Software engineering; Knowledge management; Software quality; Data science; Software development; Management","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008723586,0.0003333396,0.0003410605,0.0002007912,0.0001579156,0.0006917967,0.0008544883,0.0001841045,0.00002287776],"category_scores_gemma":[0.002407901,0.0003431807,0.00005020002,0.0008294105,0.00002361707,0.002146967,0.001472447,0.0007565267,0.00004329839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001160546,"about_ca_system_score_gemma":0.00008384765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008920286,"about_ca_topic_score_gemma":0.000005512418,"domain_scores_codex":[0.9973173,0.0001124787,0.0004014002,0.0008793153,0.0006845658,0.0006049846],"domain_scores_gemma":[0.9964161,0.002177007,0.0001580817,0.00087541,0.00008871601,0.0002847053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001740606,0.0003901759,0.9540211,0.0001331038,0.0001704009,0.001729958,0.01133297,0.0113257,0.0001955544,0.0001479407,0.0001528165,0.0203829],"study_design_scores_gemma":[0.011125,0.005350392,0.7682891,0.0005548931,0.0002192414,0.01526404,0.007378326,0.1716694,0.001482798,0.0002186308,0.01315676,0.005291312],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8611042,0.0001223471,0.1363656,0.0001165524,0.0006474541,0.0005954968,0.000003659246,0.001028015,0.00001665313],"genre_scores_gemma":[0.9677744,0.000004303331,0.03174395,0.00004612316,0.0002852507,0.0000575632,0.000004898615,0.00005095994,0.00003258606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1857319,"threshold_uncertainty_score":0.999902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1720305715459125,"score_gpt":0.3707082639811677,"score_spread":0.1986776924352552,"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."}}