Gartner defines AI code assistants as tools that generate and analyze software code and configuration. They use foundation models like LLMs, program-understanding technology, or both. Developers engage with these assistants to generate, analyze, debug, test, fix, refactor code, search dependencies, update libraries, create documentation, understand code, upgrade versions, translate languages and review commits. They help developers learn and explore codebases and access related information, such as frameworks and tools. AI code assistants integrate with developer environments, code editors, command-line terminals, chat interfaces, project management tools, monitoring, logging and deployment tools. Some are customized to an organization’s specific codebase and documentation. AI code assistants enhance software developers’ experience by boosting their efficiency, accelerating application development, minimizing cognitive overload, amplifying their problem-solving skills, enabling faster learning, fostering creativity and maintaining their state of flow.
Code review tools are software applications that help developers review and improve code quality by examining code changes, identifying issues, and ensuring adherence to standards. These tools enhance collaboration and knowledge sharing among team members, making the codebase more maintainable and reliable. Key features include enhancing code quality by automatically checking for coding standards, bugs, and security vulnerabilities. These tools allow reviewers to provide clear, actionable feedback through inline comments and streamline the integration of code changes via pull requests or merge requests. Typical users include developers, team leads, and quality assurance engineers who collaborate to maintain high code quality and streamline the development process.