{"id":"W4398239408","doi":"10.1145/3639478.3643069","title":"Interpretable Software Maintenance and Support Effort Prediction Using Machine Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Fanshawe College","funders":"","keywords":"Computer science; Machine learning; Software maintenance; Predictive modelling; Software; Support vector machine; Decision tree; Software development; Software engineering; Quality (philosophy); Automation; Artificial intelligence; Engineering","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":[0.001662023,0.0005585979,0.0003564678,0.001144204,0.0001675848,0.0009282195,0.0007765978,0.0005076305,0.001106521],"category_scores_gemma":[0.009501169,0.000202416,0.0004347033,0.0009905451,0.000247757,0.001237468,0.0003537548,0.0007624526,0.0001597743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007987897,"about_ca_system_score_gemma":0.000568077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004149777,"about_ca_topic_score_gemma":0.005427092,"domain_scores_codex":[0.9993818,0.0003002899,0.00004066455,0.0001061228,0.0001283263,0.00004286031],"domain_scores_gemma":[0.9929704,0.005538232,0.0005990436,0.0003525462,0.0004921758,0.00004755118],"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":[0.0001261719,0.0002841414,0.03542477,0.0001280064,0.0001178751,0.0001849199,0.0003485589,0.8130718,0.001769056,0.01442051,0.001242212,0.132882],"study_design_scores_gemma":[0.000002640368,0.00001612896,0.00196565,0.000006764631,0.000007886691,0.00001117818,0.00002095084,0.9933257,0.0002856978,0.004233176,0.000119777,0.000004340857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3285481,0.000252358,0.6672935,0.0004777924,0.00002673633,0.0000682632,0.0004279223,0.0007309725,0.002174301],"genre_scores_gemma":[0.9531673,0.00008691168,0.04591896,0.00002345287,0.00001129487,0.00003404425,0.0002876935,0.00001684251,0.000453561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004149777,"threshold_uncertainty_score":0.008789659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273406685091577,"score_gpt":0.2549749341644379,"score_spread":0.2422408673135222,"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."}}