AI Code Assistants: Beyond Coding

After covering the fundamentals of AI assistants which already make us consider how these tools can become indispensable in the future, it's time to explore other capabilities which extend their functionality beyond the basics of coding.

For the next post in the 'AI Code Assistants' series, we’ll explore some advanced features that help developers push boundaries, from creating technical documentation to building infrastructure and reviewing code.

Technical Documentation

Nowadays, having an API without documentation can be seen as a mortal sin. A good and detailed documentation is crucial, serving as the bridge between developers and consumers, however, manually writing and maintaining this documentation can not only be time-consuming but also tedious and prone to errors.

An AI assistant can come in handy for this type of task, particularly when working with specifications like Swagger or OpenAPI. By analysing the codebase, AI assistants can automatically generate API documentation that aligns with the defined code specs. Assistants can extract details about endpoints, data models or methods directly from our code, ensuring that the generated documentation is accurate and up-to-date.

For example, if we're building a RESTful API, an AI code assistant could not only generate the OpenAPI specs but also fill in the details about request/response formats, headers, and even authentication protocols. The best part is that the documentation can evolve alongside our code, reducing the chances of miss matches between what API offers and what your documentation claims.

Non-Technical Documentation

Beyond documenting API inputs and outputs, assistants can also help with general project documentation, such as README files, manuals, or technical reports. It's very common for many developers to neglect documentation when building a new project considering that writing this type of content can feel tedious or secondary to actual coding, however, documentation plays a key role in making a project accessible to other developers.

With AI assistants, anyone can auto-generate sections of documentation that describe the project's structure, dependencies and setup instructions. This is particularly useful for README.md files that are crucial for any project that requires collaboration. Another example is, after we've implemented a new feature, an AI assistant can help create a new section detailing how to configure and use said feature. It can also help generate user manuals that explain the functionality ensuring non-technical users can understand it effectively.

This blend of technical and non-technical documentation ensures that both developers and non-developers can interact with our software without getting lost in the details.

Infrastructure

With the rise of DevOps, infrastructure already plays a huge role in software development. Containerisation has become a key part of modern software deployment, and while creating Dockerfiles isn’t particularly difficult, ensuring they are optimised can be a challenge. An AI assistant can help with the creation of infrastructure components such as generating Dockerfiles or docker-compose based on project’s dependencies, runtime environment, and other configurations.

It can also suggest improvements, such as reducing image size by combining RUN commands or choosing a more efficient base image. For multi-container applications, AI can generate a docker-compose file that defines how services will interact, including networks, volumes, and environment variables.

Beyond Docker, AI can assist in generating configurations for other infrastructure-as-code (IaC) tools such as Kubernetes manifests or Terraform scripts. These capabilities enable developers to set up robust, scalable infrastructure with minimal effort, speeding up the process of moving from development to production.

For example, if you're building a micro-service architecture, the assistant could help create a fully functional docker-compose file that configures the various services, databases, and network layers, at the same it ensures compatibility with our system’s specifications.

Code Analysis and Review

One of the most valuable features of AI code assistants is their ability to perform code analysis and assist in code reviews. Traditionally, code reviews rely heavily on the experience and attention of reviewers, which can sometimes miss problems or produce biased results. AI can help by providing an additional layer of security, analysing the code for potential problems, inefficiencies and even vulnerabilities.

These assistants can scan throughout the entire codebase, flagging areas of improvement such as unused variables, inefficient loops, or risky operations. They can even suggest improvements and refactoring's that could improve performance or readability, offering recommendations that align with best practices and modern standards.

When used during a code review, assistants can complement the human review by identifying patterns or issues that might have been overlooked, such as subtle bugs, outdated libraries, or even security flaws like SQL injection. In certain cases, they could also enforce better quality standards by automatically detecting violations of coding style guidelines.

Conclusion

From automatically generating API documentation and infrastructure components to offering intelligent code analysis and review, these tools are making the development process more approachable and complete.

As AI continues to evolve, we can expect these assistants to become even more powerful and shape the way we write, review, and maintain code. Whether we're looking to improve productivity, code quality, or streamline our infrastructure setup, embracing AI assistants can offer a considerable boost in efficiency.

Stay tuned for further insights!