AI Code Assistants: Features Overview

Following the previous chapter in the 'AI Code Assistants' series, we're now set to explore the features that are reshaping how we approach software development using AI, making coding faster and possibly more efficient.

In this post, we'll delve into the key features of AI code assistants showcasing how they can enhance the development workflow by integrating directly into our day to day tools.

Embedded Agent

One of the most practical advantages of AI Code Assistants is how seamless they integrate with IDEs like Visual Studio Code or IntelliJ IDEA, providing an embedded AI agent directly into our coding environment.

Embedded Chat agent - (Llama, Copilot and Amazon Q)

For those already using an AI assistant, these eatures comes in handy considering that it greatly reduces the amount of times one has to switch between different tools or windows, allowing us to get access to fairly recent documentation, examples, refactoring existing code or simply using it as troubleshooting tool, right at our fingertips.

Being embedded in the IDE allows these assistants to operate at full capacity, since they are able to collect details about the project such as code standards, patterns from existing code, what libraries are used in the project, etc. Taking into consideration the environment details, greatly improves the accuracy when providing real-time suggestions.

Code Autocomplet

Autocomplete is probably one of the most familiar and widely used features in IDEs since the old days. Traditionally, autocomplete functionality offers intelligent and context-aware suggestions when we are typing in the IDE, and perhaps, most of us that have used this feature before can relate with the frustration of how limiting or imprecise suggestions can be.

Autocomplete driven by AI sets itself apart from the basic IDE syntax suggestions with its ability to understand complex patterns and dependencies used in our code. For example, if we’re working within a particular framework, the AI assistant can suggest methods or variables that are framework specific, without having to remember all of that syntactic sugar from the top of our heads.

This feature greatly speeds up the coding process, however, it should be used with caution because it can also increase the likelihood of errors or some of its information might be out of date.

Code Suggestion

Beyond simple autocompletion, AI code assistants also offers more advanced code suggestions. These are not just snippets of code but entire blocks or even full functions tailored to the task at hand. As an example, imagine you're writing a function to handle user input validation, the code suggestion might propose a validation route based on the best practices, including error handling and edge case that wouldn't be otherwise considered.

Refactor tests - Llama with Continue.dev

These suggestions are particularly useful when you’re working on something new or unfamiliar. Instead of searching through documentation or Stack Overflow on how to implement a certain feature, the AI can provide a solution directly in your IDE. This capability not only saves time but also helps maintain a consistent coding standard across your project.

Generate Code from Comment

A more advanced feature that is naturally embedded into the developer workflow is the ability to generate code from comments. This feature allows developers to write a code comment using natural language describing what they are aiming in terms of functionality, and the AI then generates the corresponding code.

For example, you might write a comment like the following and AI would generate the appropriate code to fulfil the request:

Generate test from comment - Llama with Continue.dev

This is particularly useful as it allows you to quickly sketch out functionality without getting involved into the details of implementation. It's sort of a bridge between design and implementation, making it easier to translate ideas into working code, and it's particular useful when exploring new languages that we don't yet feel fully comfortable with.

Bug Fixing and Optimization

Very often, developers find them self spending an huge amount of time trying to understand why the code is not working, making debugging one of the most time-consuming parts of software development. AI code assistants can help streamline this process by identifying bugs and suggesting fixes. By analyzing your code for common errors, performance issues, or security vulnerabilities, these tools can highlight potential problems and even propose solutions.

As an example, if there’s a potential problem or a performance bottleneck in your code an AI assistant
can help track down the problem and suggest a way to fix it. By making use of the embedded agent in our IDE, we can speed up the process of finding similar problems, whether we ask related questions or we simply paste a stack trace of a specific problem, AI assistants can be invaluable in catching issues early, before they become harder to resolve or make their way into production.

Test Creation

At Codurance we take Test Driven Development (TDD) very serious. This software development practice ensures that our code is thoroughly tested and helps catch bugs early in the development cycle. As part of this process, writing all kind of tests is a crucial part of our daily job. Once again, AI code assistants can help with the automation of this process by generating test cases based on natural language inputs.

Even when TDD is not in the picture, we can still ask AI to generate a suite of tests for an existing function that cover various input scenarios, edge cases and expected outcomes. This not only saves time but also ensures that your codebase has robust test coverage, which is essential for maintaining quality and reliability as your project grows.

Contextual Awarenes

The effectiveness of an AI code assistant depends largely on its ability to understand the broader context of our project. This includes recognising patterns in our code, understanding project-specific conventions, analysing existing tools and libraries that we are already using.

Imagine that we’re working on a large project with multiple modules, the AI can understand the relationships between those modules and provide suggestions that are consistent with the overall design and architecture of the system.

This level of contextual understanding makes the AI assistant more than just a tool for writing code, it becomes a highly valuable partner in the development process, helping to improve the consistency and quality of our project.

Conclusion

In this article of AI Coding Assistants series we explored the different types of features it offers and how they can represent a significant enhancement to the regular development workflow. From code suggestions to advanced capabilities like bug fixing, test creation, and code generation from comments, these tools are designed to make coding faster, easier, and more efficient.

Integrating an AI code assistant into your IDE can really improve the experience of writing code, and considering how AI continues to evolve, such features will become even more powerful and intuitive in the future.

In the next post, we'll explore advanced use cases for such features and how they integrate with other developers workflows such as preparing infrastructure or developing comprehensive documentation.

Stay tuned!