{"id":"W2951710749","doi":"10.1109/tse.2019.2924006","title":"Locating Latent Design Information in Developer Discussions: A Study on Pull Requests","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Université de Montréal; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Maintainability; Classifier (UML); Documentation; Software engineering; Software design; Machine learning; Software; Robustness (evolution); Source lines of code; Artificial intelligence; Data mining; Software development; Programming language","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009141678,0.0004587939,0.0005244128,0.004007635,0.001635215,0.001775787,0.0008543476,0.001457177,0.001332165],"category_scores_gemma":[0.09755081,0.0003661175,0.0004147217,0.002545913,0.001053556,0.004016744,0.001820184,0.001653197,0.000867318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009655558,"about_ca_system_score_gemma":0.0005779243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002327137,"about_ca_topic_score_gemma":0.003867551,"domain_scores_codex":[0.9900956,0.005954613,0.0007297847,0.0009544624,0.001893653,0.0003718432],"domain_scores_gemma":[0.7545282,0.2045784,0.01777109,0.006790863,0.01405992,0.002271464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001435569,0.002247733,0.7484977,0.0008249818,0.000135155,0.00150444,0.09498899,0.001256273,0.01601887,0.001511595,0.004763383,0.1268155],"study_design_scores_gemma":[0.0001375022,0.002160643,0.8552497,0.0003668228,0.0001495728,0.002740694,0.05727635,0.04226146,0.01282671,0.003572237,0.02305809,0.0002002378],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994009,0.0001893147,0.003926924,0.0001828311,0.00001007405,0.00006003419,0.0002308752,0.0001071063,0.001283892],"genre_scores_gemma":[0.9932188,0.0001466108,0.003761887,0.0001315471,0.00003226074,0.0001073282,0.001003546,0.00009901298,0.001499072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009141678,"threshold_uncertainty_score":0.0483464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02160847518558152,"score_gpt":0.2497984487397829,"score_spread":0.2281899735542013,"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."}}