Analysts associate the risk of not completing on time and within the allocated budget of IT projects with in-effective quality control. Scientific approaches to quality control in IT projects often rely on economic methods or are reactive in nature, failing to allow for the early detection and prevention of errors, failures, or malfunctions during the implementation of an IT project. The features of quality control in IT projects formulated. The managerial, technical, and social subsystems of the IT project as an element of the IT company's organizational system are identified. The problems of quality control in IT projects, such as the accumulation of technical debt, reactive control, and conflicts between quality and development speed, are structured and described. Their features, which require the application of modeling methods to improve management efficiency, are highlighted. A conceptual structure of the quality control system for IT projects is presented, based on organizational systems theory and integrating simulation and neural network modeling. The processes of quality control in IT projects are formally described: the process of team and process organization, the process of development and functional changes, the process of testing and quality control, and the process of managing technical debt and architecture. Decision-making models from the perspective of organizational systems theory are provided for the center (project manager), controlled entities (quality control processes), and the control object (IT project). Simulation modeling serves as a “digital twin” for analyzing various implementation scenarios of the IT project, while neural network models provide predictive analytics and processing of nonlinear factors. A quality control scheme based on the integration of simulation and neural network models is described, where the models mutually enrich each other with synthetic and real data, facilitating the implementation of adaptive project management in an IT company. A detailed algorithm for integrating the IT project quality control system based on the agile approach and simulation and neural network models is presented. The approach is focused on transitioning from reactive to proactive control, enhancing efficiency under conditions of stochasticity and uncertainty in key IT project implementation processes.
Shevchenko et al. (Fri,) studied this question.