{"id":"W4205540067","doi":"10.38028/esi.2021.24.4.009","title":"REENGINEERING TECHNIQUE ADAPTATION OF LEGACY SOFTWARE SYSTEMS","year":2022,"lang":"ru","type":"article","venue":"Информационные и математические технологии в науке и управлении","topic":"Engineering Education and Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Siberian Branch, Russian Academy of Sciences; Russian Foundation for Basic Research; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Business process reengineering; Computer science; Adaptation (eye); Legacy system; Software system; Process management; Software; Software engineering; Systems engineering; Engineering; Manufacturing engineering; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001783281,0.0009938023,0.001290924,0.001934406,0.0007746721,0.0003781547,0.003620357,0.0005285202,0.0005165763],"category_scores_gemma":[0.0005052176,0.001238599,0.0004541978,0.003852937,0.0002536823,0.00117475,0.001929848,0.00206607,0.0001279408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009173887,"about_ca_system_score_gemma":0.001141083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007369013,"about_ca_topic_score_gemma":0.00001655621,"domain_scores_codex":[0.992832,0.0004940365,0.001941663,0.00162489,0.001651651,0.001455729],"domain_scores_gemma":[0.9946245,0.0004363758,0.001088868,0.002866586,0.0005642158,0.0004194723],"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":[0.0001176871,0.002047465,0.00128327,0.00250494,0.0006241126,0.0002548218,0.005618943,0.6081592,0.01387457,0.2985952,0.01305884,0.05386095],"study_design_scores_gemma":[0.003590886,0.002867827,0.003304974,0.001254756,0.000342029,0.001866373,0.007644423,0.578271,0.01438049,0.003021443,0.3787243,0.004731423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01710754,0.009248054,0.9545093,0.001199616,0.00993247,0.002483829,0.000216683,0.002446952,0.002855537],"genre_scores_gemma":[0.9578142,0.0001710432,0.0370414,0.0001368221,0.0003961767,0.001400408,0.0001024986,0.0002148644,0.002722576],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9407067,"threshold_uncertainty_score":0.9990064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01523972855333165,"score_gpt":0.2219225731209899,"score_spread":0.2066828445676583,"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."}}