{"id":"W3118614203","doi":"10.1109/tse.2020.3048335","title":"Accelerating Continuous Integration by Caching Environments and Inferring Dependencies","year":2020,"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 Waterloo; McGill University","funders":"Mitacs","keywords":"Computer science; Acceleration; Service (business); Dependency (UML); Task (project management); Distributed computing; Process (computing); Software; Software engineering; Operating system; Systems engineering","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":[0.00298488,0.001826297,0.000842821,0.005907256,0.0008571355,0.002557745,0.003026569,0.0008539297,0.00103744],"category_scores_gemma":[0.01995222,0.001746815,0.001443924,0.006035126,0.001065186,0.004701651,0.00308157,0.002039092,0.001399382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001141712,"about_ca_system_score_gemma":0.002772157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01903348,"about_ca_topic_score_gemma":0.04826032,"domain_scores_codex":[0.9952226,0.0008012651,0.0004635814,0.00136569,0.001785865,0.000361008],"domain_scores_gemma":[0.985,0.005106078,0.001711836,0.005600207,0.002183377,0.0003984941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008130749,0.0006225033,0.2622169,0.001219123,0.0006241226,0.0008492937,0.002896249,0.1735685,0.02837637,0.01087132,0.04624509,0.4716974],"study_design_scores_gemma":[0.00008259677,0.0002263818,0.04543383,0.0001475629,0.0003178969,0.0004133298,0.0006333992,0.8684969,0.03147016,0.01416347,0.03845137,0.000163098],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4871252,0.002816434,0.3697951,0.001197232,0.0001494588,0.000386345,0.0275226,0.1031041,0.007903511],"genre_scores_gemma":[0.4923242,0.0007277731,0.4280152,0.0002540534,0.00005253864,0.0002756574,0.07251174,0.003822793,0.002016132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01903348,"threshold_uncertainty_score":0.03784543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0184517249869039,"score_gpt":0.2205484944750087,"score_spread":0.2020967694881048,"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."}}