{"id":"W4386442953","doi":"10.1145/3617172","title":"On the Caching Schemes to Speed Up Program Reduction","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reduction (mathematics); Debugging; Compiler; Cache; Parallel computing; Process (computing); ENCODE; Memory footprint; Encoding (memory); Compile time; Computation; Theoretical computer science; Computer engineering; Algorithm; Programming language; Artificial intelligence","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":[0.002895823,0.001296575,0.00103098,0.002525056,0.001254443,0.001916954,0.003776266,0.001210505,0.00348002],"category_scores_gemma":[0.02042489,0.0008153924,0.001105609,0.00339477,0.0028206,0.00988454,0.002476002,0.002463872,0.0009089352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002881984,"about_ca_system_score_gemma":0.004294496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005671298,"about_ca_topic_score_gemma":0.006142795,"domain_scores_codex":[0.9955525,0.001173102,0.0003366101,0.0006451344,0.001618826,0.0006738981],"domain_scores_gemma":[0.9788353,0.009344569,0.001649957,0.00794138,0.001923307,0.0003054949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001078428,0.000405332,0.007951087,0.00105074,0.0001427265,0.0003756725,0.0009589081,0.1012108,0.05932866,0.2553092,0.01360139,0.5585872],"study_design_scores_gemma":[0.000197346,0.0009363694,0.002831294,0.0004631087,0.0003573363,0.001054891,0.0003581271,0.7359226,0.1094725,0.1106603,0.03757716,0.0001688498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1768248,0.01219809,0.786121,0.002923278,0.0002765843,0.0004778983,0.0003196847,0.008951737,0.01190693],"genre_scores_gemma":[0.5848091,0.003732988,0.4040267,0.0007432545,0.0002412137,0.0004069814,0.0004865196,0.001015809,0.004537457],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005671298,"threshold_uncertainty_score":0.02091038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1289523923715804,"score_gpt":0.3560371859268365,"score_spread":0.2270847935552561,"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."}}