Multi-objective optimization of software development processes using large language models as assistants

This article examines the problem of managing the use of large language models based on transformer architectures in software development processes. The relevance of the study is that the implementation of Large Language Models (LLMs) in software development is often accompanied by inflated expectations: the expected reduction in development time is not always achieved, while the risks of reduced quality, increased costs, vulnerabilities, and the need for additional verification of the result remain. The subject of this study is the use of LLMs in software development as a managed mode of project work. The scientific novelty of the proposed approach lies in presenting the use of LLMs not as a standalone code generation tool, but as a multi-objective model for selecting a rational development mode. The aim of this study is to develop a multi-objective approach to managing the use of LLMs in software development, taking into account time, cost, result quality, and risks. The methodological basis includes the analysis and systematization of scientific publications, the formalization of factors influencing the effectiveness of LLM use, multi-objective modeling, and the Pareto optimality principle. The study identifies the following controllable parameters of LLM application scenarios: the level of task decomposition, the degree of elaboration of project documentation, the quality of the problem statement and prompt, and the selected language model or a tool based on it. In addition, contextual parameters are identified: developer or team qualification, programming language, and technology stack. The identified parameters are shown to affect the time required for documentation preparation, result generation, code review and correction, as well as the cost, quality, and risks of the project. The experimental part uses scenario modeling of alternative LLM application modes, which allows comparing options according to time, cost, quality, and risk criteria. The proposed model enables the selection of rational LLM application scenarios on the Pareto set, taking into account the constraints of a specific IT project. Unlike existing approaches mainly focusing on evaluating individual AI tools, generation quality, or developer productivity, the proposed model considers LLMs as an element of a managed software development process. The practical application of the approach involves adapting IT project management tools: task visualization and routing tools, prompt-oriented documentation templates, and a registry of informal notes. This reduces the variability of generated results and improves the manageability of LLM use in software development.