{"id":"W6891789831","doi":"10.48550/arxiv.1007.5115","title":"Bayesian Network Based XP Process Modelling","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Software Engineering Techniques and Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Bayesian network; Extreme programming; Process (computing); Software; Dynamic Bayesian network; Bayesian probability; Software development","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":[0.0003871829,0.0003236138,0.0002769693,0.0001684785,0.0001616719,0.0002156544,0.002286865,0.0004767621,0.00002683923],"category_scores_gemma":[0.00003296802,0.0003818289,0.0001606595,0.000564259,0.00004352332,0.0004862225,0.0007942998,0.001332951,0.00001762474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006457712,"about_ca_system_score_gemma":0.0002317934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008536415,"about_ca_topic_score_gemma":0.00001169325,"domain_scores_codex":[0.9982892,0.00006885725,0.0001690443,0.0009602343,0.0001044665,0.0004081502],"domain_scores_gemma":[0.997843,0.0002276439,0.0002405234,0.001372664,0.0001455648,0.0001706407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008781179,0.00002705858,0.0003949473,0.00007255904,0.00002088347,0.000106962,0.00004155811,0.968658,0.000001223424,0.03018535,0.0001615438,0.0003211828],"study_design_scores_gemma":[0.0001040201,0.00002248115,0.0000210505,0.00009803454,0.00003215628,0.000002751044,0.000002520619,0.9281147,0.00007179113,0.06928609,0.001833731,0.000410702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00874918,0.00005074017,0.9875361,0.0001109137,0.0008055103,0.0002319678,0.000003690008,0.001485625,0.001026231],"genre_scores_gemma":[0.8479973,0.00004504355,0.1515181,0.00009712892,0.0001712987,0.000002091128,0.000008058661,0.00002531698,0.0001357173],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8392481,"threshold_uncertainty_score":0.9998634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06038898811416872,"score_gpt":0.1958923771792231,"score_spread":0.1355033890650544,"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."}}