{"id":"W6903344804","doi":"10.1109/ccwc62904.2025.10903859","title":"End-to-End Requirements Mapping for Cloud-Native Applications Using ML Models","year":2025,"lang":"en","type":"article","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada)","funders":"","keywords":"Component (thermodynamics); Cloud computing; Software deployment; Microservices; Reliability (semiconductor); Artificial neural network; Key (lock); Virtual network; Performance indicator","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":[0.0008743381,0.001693035,0.0005469958,0.001041822,0.0003885149,0.00144902,0.001142676,0.0009445975,0.002547234],"category_scores_gemma":[0.004305451,0.0005699456,0.0007333408,0.0007274828,0.0004144459,0.001716688,0.0009224183,0.001505848,0.0008682933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002458778,"about_ca_system_score_gemma":0.001735905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02182542,"about_ca_topic_score_gemma":0.03093311,"domain_scores_codex":[0.9992265,0.0002150593,0.00004043088,0.0002241346,0.000180864,0.0001131704],"domain_scores_gemma":[0.9982823,0.0009971246,0.0001815835,0.0001310306,0.0003310631,0.00007686148],"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":[0.00006280117,0.00006306254,0.001566594,0.00004824238,0.00002116335,0.00008245789,0.00003941377,0.9683881,0.00103179,0.00185052,0.001268016,0.02557773],"study_design_scores_gemma":[0.000001363033,0.000003705059,0.0001018321,0.000002251711,0.000001468252,0.00000522119,0.000005896033,0.9982529,0.0002206244,0.001275907,0.0001269615,0.000001755027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1113153,0.0004332542,0.8736183,0.0009050312,0.00006307557,0.0001579489,0.001288434,0.004923045,0.007295507],"genre_scores_gemma":[0.8713413,0.0002344661,0.1220479,0.0003167788,0.00003461278,0.0001784571,0.001988618,0.0003776009,0.003480255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02182542,"threshold_uncertainty_score":0.04339671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08025846821248689,"score_gpt":0.3218731714079177,"score_spread":0.2416147031954308,"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."}}