Модуль інтелектуалізованої підтримки розробки програмного забезпечення

The fast-paced information technology market demands rapid and high-quality software development to stay competitive. However, routine tasks such as code documentation, refactoring, test creation, and ensuring S.O.L.I.D. principle compliance consume a significant amount of developer time, with studies showing that up to 50% of effort is spent on such activities. Existing tools, such as GitHub Copilot and Tabnine, offer partial automation but lack comprehensive S.O.L.I.D. analysis, flexible AI model selection, and seamless integration within environments like Visual Studio Code. This highlights the need for a robust solution to streamline workflows, aligning with the growing use of AI assistants to boost coding efficiency and quality.This study tackles these challenges by introducing the Smart AI Code Assistant, a VS Code extension that automates routine tasks using AI models such as GPT, Claude, Gemini, Grok, and DeepSeek. The research aims to enhance developer productivity through automated documentation, refactoring, unit test generation, and S.O.L.I.D. compliance checks within a unified interface. Unlike other tools, the module allows task-specific AI model selection for optimal speed, accuracy, and cost, and provides detailed S.O.L.I.D. analysis with actionable feedback, improving code architecture.The methodology involved analyzing automation trends, evaluating AI model capabilities, and developing S.O.L.I.D. verification methods. Built with JavaScript and Node.js, the module uses Tree-sitter for code analysis and supports languages like JavaScript, Java, and Python. Key features include safe documentation generation, modular refactoring, test integration, and S.O.L.I.D. violation reports with fixes. Experiments tested refactoring performance on a flawed JavaScript TaskManager class across 100 trials, assessing test pass rates and response times.The results of the experiments demonstrated varying effectiveness of AI models. Claude-3-7-sonnet and deepseek-chat achieved 100% test pass rates, with Claude faster (10.81 vs. 33.14 seconds). Gemini-2.0-flash balanced speed (4.24 seconds) and accuracy (97.75%), offering cost-effectiveness. The module’s cohesive VS Code integration reduces manual effort and enhances code quality. It is a practical tool with potential for expansion to languages like C# and Go, and CI/CD integration. The Smart AI Code Assistant advances software development by addressing existing tool limitations, enabling faster, higher-quality outputs.