{"id":"W4378676681","doi":"10.1109/icstw58534.2023.00069","title":"Test Cost Reduction for 5G and Beyond using Machine Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); University of Ottawa; Carleton University","funders":"","keywords":"Reduction (mathematics); Test (biology); Computer science; Cost reduction; Machine learning; Artificial intelligence; Mathematics; Geology","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":[0.001414701,0.001028534,0.0006627,0.001705494,0.0002844268,0.001057983,0.001415533,0.0006603568,0.003724874],"category_scores_gemma":[0.008126046,0.0001891349,0.0006614797,0.001036292,0.0003473756,0.001376457,0.0006378763,0.0008746194,0.0005906828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001192255,"about_ca_system_score_gemma":0.001287228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005637622,"about_ca_topic_score_gemma":0.005161824,"domain_scores_codex":[0.9983938,0.0005168419,0.0000777529,0.0002041156,0.0006648671,0.0001426123],"domain_scores_gemma":[0.9946545,0.003033714,0.0005018893,0.0007593531,0.0008952265,0.0001553021],"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":[0.0005907098,0.0003475088,0.01130782,0.0002388375,0.00009892476,0.0003260371,0.00006531721,0.2310338,0.02454839,0.005569567,0.004106984,0.7217661],"study_design_scores_gemma":[0.00004546414,0.0003667563,0.006131324,0.00005986765,0.00006185853,0.0002332609,0.00006208466,0.9632603,0.01671055,0.009861897,0.003184536,0.00002216303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3616947,0.002416469,0.6122404,0.002339313,0.0001396867,0.0002970699,0.0006565821,0.008355625,0.01186015],"genre_scores_gemma":[0.8392486,0.0003200361,0.1578857,0.0001709623,0.00002804843,0.00007066762,0.0005846148,0.0002391663,0.00145215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005637622,"threshold_uncertainty_score":0.01246095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04976053073262066,"score_gpt":0.3063258694206697,"score_spread":0.2565653386880491,"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."}}