With software capable of writing itself, are we on the verge of a new paradigm in software development? What are the implications?
It can be used to test alternative solutions to the same problem, such as comparing notes with another software engineer.
In addition to helping with code, AI assistants open up a new space for developers, who can use them to write code in new languages and frameworks and learn as they go. It's another way to approach new technologies without having to spend hours flipping through pages and pages of documentation. Do you need to learn how to rotate a matrix in a new language? Just comment and let the copilot do his job.
Of course, as with any tool, it can be misused. Yes, a software developer can become dependent on an AI assistant to write their code. Or they could stop doing full code reviews, overestimating their friendly helper, and letting unintentional bugs creep into the final product.
It's just common sense to determine best practices regarding AI assistants before implementing them as a solution. It may be tempting to dive head first into technology, but it's probably safer to start small. Run tests or implement it on a small project first and get feedback, find out how developers and project leads felt about the tool, and make an informed choice based on the results.
While technology is extremely useful for junior developers, it's a good idea to have a senior developer provide input and feedback. This way, they can help newcomers build good habits.
Disadvantages of metaprogramming
While I'm a big proponent of AI assistants, there are a few things to keep in mind. Firstly, no matter how large your data sample is, there is always the possibility of bias. What does that mean?
Some communities may develop and share bad practices for a variety of reasons. And these practices can enter the algorithm. Code reviews can catch the problem, but if you rely too much on the wizard, you may assume the code is perfect as is. This can lead to a range of problems, from bugs to security issues.
Security is another point that we must keep in mind. Copilot and similar products use open-source semantic cues that may have outdated API calls and very little security. Additionally, anyone with the same wizard, with the right queues, will be able to discover a company's source code if they use the unedited output. Unlikely? Yes. Impossible? No.
Finally, there is also the issue of intellectual property , which is a whole new can of worms. Let's just say that generative code is likely to be a huge legal headache for years to come.
So should we jump on the bandwagon? I would cautiously say yes. As long as you train your team and set a best practice guide to follow, AI assistants can be a huge asset to better and faster software development.
