{'fraction_non_alphanumeric': 0.012682926829268294, 'fraction_numerical': 0.003902439024390244, 'mean_word_length': 5.470031545741325, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Analytical prediction of the integral risk of violation of the acceptable performance of the set of standard processes in a life cycle of highly available systems Part 1. Mathematical models and methods

With the widespread adoption and development of the process approach, it became clear that the standard processes used in the life cycle of highly available systems undoubtedly have a cumulative impact on risks that arise. However, the possibilities for predicting risks in practice are significantly limited: private and integral risks of violation of the acceptable performance of implemented processes, estimated by simplified methods, do not reflect the real picture, and specialized models of specific systems and processes require painstaking and long-term scientific and methodological study. Thus, there was a critical methodological contradiction between objective needs and real capabilities in predicting private and integral risks. Carrying out a scientific search for ways to eliminate the identified contradiction, the main goal of this work (in two parts) is to create scientifically based methodological and software-technological solutions for analytical prediction of the integral risk of violation of the acceptable performance of a given set of standard processes in the life cycle of systems. In the first part of the work, for 30 standard processes defined by GOST R 57193 (characterized by typical actions and real or hypothetical input data for modeling and linked to possible scenarios for their use in the creation and/or operation and/or disposal of systems), mathematical models and methods for predicting the integral risk of violating the acceptable performance of a given set of standard processes with the possibility of traceable analytical dependence on influencing factors are proposed. The second part of the work is devoted to the description of the proposed software-technological solutions for risks prediction using models and methods of the first part for solving practical problems of system engineering.