{"id":"W3204491293","doi":"10.1109/tse.2021.3115772","title":"Characterizing and Mitigating Self-Admitted Technical Debt in Build Systems","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Technical debt; Software engineering; Operating system; Software; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007643808,0.0006584677,0.0004802269,0.006545451,0.001349102,0.002805658,0.0008169482,0.001113648,0.0004554142],"category_scores_gemma":[0.07248676,0.0005456097,0.0004571172,0.002986351,0.001194299,0.004127734,0.002787676,0.00112089,0.0002893451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001335374,"about_ca_system_score_gemma":0.001535728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005286327,"about_ca_topic_score_gemma":0.0102347,"domain_scores_codex":[0.988746,0.003531812,0.001383362,0.001353923,0.004320341,0.0006645067],"domain_scores_gemma":[0.8964874,0.05310795,0.02912112,0.005825181,0.01392853,0.001529856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002287148,0.000194204,0.7880252,0.0009881782,0.00009782507,0.001272492,0.03580893,0.004197549,0.02327444,0.0015322,0.001916013,0.1424642],"study_design_scores_gemma":[0.00002240977,0.0004313143,0.8448782,0.0006526834,0.0001473708,0.002309386,0.02497714,0.0873808,0.017601,0.004895234,0.0165105,0.0001939843],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729862,0.0004298349,0.02413975,0.0003070307,0.00001797548,0.0001171094,0.0002878101,0.000456807,0.001257595],"genre_scores_gemma":[0.9773691,0.0001949403,0.02048814,0.00009818897,0.00002323111,0.00008537016,0.0008665655,0.000131675,0.0007427561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007643808,"threshold_uncertainty_score":0.04042476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009912218925696303,"score_gpt":0.2297641678374545,"score_spread":0.2198519489117582,"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."}}