5 Leading AI Coding Tools for Engineering Teams
Leading an engineering team, but feeling swept away by the sheer pace of AI development? It isn’t just you. Leveraging artificial intelligence will very soon be a requirement for businesses like yours to stay ahead.
But how do you make the right decisions today to safeguard your team tomorrow?
There are a plethora of AI tools that claim to write high-quality code for software engineers. Many arose off the back of the ChatGPT LLM (large language model) explosion. Engineering is a discipline where efficiency and creativity need to meet in order for teams to stay ahead.
In this blog post, I’ll explore 5 AI code assistant tools that are making waves. I’ll talk about the big one – GitHub Copilot X – and, especially while we wait for it to launch in beta, some Copilot X alternatives. Some of these tools are more specialised, with more specific use cases, and some are more experimental than others.
Why AI-Powered Code Generation Matters
Efficiency is a buzzword in our world. There’s more pressure on the resource of engineering teams than there has been for quite some time. The demand for automation in software engineering is skyrocketing, and AI-powered code generation is stepping up to the plate.
By reducing the time spent on repetitive tasks and enhancing code quality, AI code generation tools enable engineers to focus on more complex, high-level work. This shift paves the way for faster development cycles and more efficient teams.
AI tools serve as an enhancement to human capabilities, not a replacement. By embracing AI code assistants, software engineers can combine their creativity and problem-solving skills with the power of artificial intelligence for an unbeatable combination.
1: GitHub Copilot X - AI-Powered Pair Programming
GitHub Copilot X is the best-known AI tool for software delivery – and it isn’t even out yet!
GitHub position it as AI-powered pair programming. We’re pretty confident that it will be a powerful generalist tool when it arrives, but at the time of writing, we don’t really know when that will be yet.
In the time you could wait for Copilot X to arrive, your competitors could be getting the edge, and that’s why I think it’s important to stay ahead of the curve before it arrives.
Copilot X builds upon the success of GitHub Copilot. It will leverage the power of GPT-4 to provide a more advanced AI pair programmer experience. They say it will integrate into various stages of software development workflows, promising to revolutionise how your engineers approach coding tasks.
Among its numerous features, Copilot X says it is developing features for explaining code snippets, code completion tool, fixing errors, generating unit tests, and writing pull request templates. It’s poised to streamline software delivery and enhance team productivity across code-centric tasks – as long as it gets a beta out before the competition sweeps them away…
My view: This will be the benchmark in coding AI when it is released, though I expect specialised tools to do a better job of niche areas.
2: Tabnine - The established AI coding assistant
Tabnine is an established AI code assistant for engineers. It’s been around since 2018, originally building on GPT-2 – at the time of writing, it’s built on GPT-3.
I see this as a strength and a weakness. On the one hand, Tabnine is far less experimental than other coding AI tools in this list. It’s a much better thought-out product, having had five years to evolve, and has a bunch of bells and whistles (many of which are valuable) that organisations might want to leverage. It’s totally transparent about what it’s trained on and is more legally robust. It can also run locally out of the box and accommodates various security and compliance requirements.
That said, it won’t be news to you that GPT-3.5 and GPT-4 are radically better at reasoning. Organisations leveraging these tools have access to much more powerful AI.
My view: I suspect they’ll be working away at a GPT upgrade behind the scenes. They’ll have a lot to work out, but they get on top of that, they’ll have the advantage of 5 years of prior experience.
3: Sourcegraph Cody - Read, write and understand code faster
Cody is Sourcegraph’s coding AI offering. It’s an AI code assistant designed to turbocharge your coding experience with exceptional speed and efficiency.
It’s all about empowering developers to read, write, and comprehend code. They say the gains are up to 10x, though they don’t substantiate this.
They specify that their AI can understand your whole code base, code graphs, and company documentation, delivering valuable insights and answers in real time.
My view: “Often magical, often frustratingly wrong...but getting better quickly.” Cody’s own words – but I don’t think it’ll take them long to get mistakes down to a negligible percentage. This already appears to be working for them.
4: Mutable AI - Build fast with AI
Mutable AI is on a similar mission with their AI-driven code assistant – accelerate software development.
Their features include AI autocomplete – a specialised neural network. It’s meant to eliminate the need for boilerplate code and time-consuming searches. Engineers can use prompt-driven development to refactor and ship faster.
It works with a whole bunch of popular languages (probably the ones you use), but is limited to VS Code right now. It works with Jupyter and GitHub at the time of writing.
My view: Mutable seem to have got a limited number of features to a point of being very impactful. Some features are still in Beta at the time of writing such as the refactoring module, and they haven’t got released their testing module yet. Potentially a good alternative for teams looking for an early alternatives to GitHub Copilot X.
5: CodiumAI - AI test-writing
Codium is an AI test-writing assistant that generates meaningful tests to maintain code integrity while saving developers time and effort.
I’ve found that some early versions of test generators can create pretty trivial tests. Codium uses AI to make sure you get non-trivial tests (and trivial ones, too!)
By analyzing your code, docstrings, and comments, CodiumAI intelligently suggests tests as you code, requiring only your review, acceptance, and commit to ensure thorough testing.
My view: There are limited options for testing on the market right now, but Codium are well ahead of that game and out of Beta. An obvious choice if testing is a time-drain – as it is for many engineering teams.
Bringing it all together with a collaboration tool
Most software engineers spend hours every week just keeping up with Slack, chasing progress updates. So I’m working on a product that will fill in the gap and shave hours of this non-technical work off our weeks.
CollabGPT is the AI companion for people who work on software projects.
It tracks everything that happens in Jira, GitHub and Slack, surfaces important information, makes actionable suggestions on what to do next and can answer any question about what’s happening in your projects.
It’s not just a dumb layer between an LLM and your data. It uses complex AI agent architecture to give you access to long-term “memory” and deep understanding of the context of your projects.
AI-driven code assistants are carving their niche in the world of software engineering, and CTOs and those with similar leadership responsibilities must strategically evaluate and adopt these cutting-edge tools to stay competitive.
The future is bright for AI-powered software development, and embracing the right tools today will ensure your team's continued success in the rapidly evolving landscape. From speeding up coding tasks to generating meaningful tests and revolutionizing the way engineers approach coding, AI-driven code assistants are redefining software engineering. Don't let your team fall behind; invest in the right AI tools and watch your engineering team soar to new heights.
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