AI code quality: 10 essential checks AI skips without a gate

AI is a marvel at generating code, no doubt. But it's not foolproof, far from it. Often, while churning out lines of code, it forgets to do its homework, skipping fundamental steps that ensure software quality and security.

Imagine a super-fast assistant writing a book, but skipping proofreading and spell-checking. The result? A text full of errors, maybe brilliant, but unusable. The same happens with AI code: it's quick, but unsupervised, it's a disaster waiting to happen.

A recent study by Debashish Ghosal on Dev.to highlighted how artificial intelligence tends to skip 10 key checks in the software development lifecycle.

In its creative enthusiasm, AI tends to overlook the more 'boring' yet vital checks. We're talking about security tests, code quality analysis, performance, or compliance with specific standards. These are all crucial steps for software that works well and doesn't expose you to risks.

Think about it: code that fails a security test is an open door for hackers. Slow code frustrates everyone and wastes resources. AI doesn't have the 'awareness' of these implications; it focuses only on pure functionality. It's a bit like a child building a tall tower without thinking about the foundations.

For example, input validation, ensuring user-entered data is correct and safe, is often neglected. This oversight can lead to serious vulnerabilities, like SQL injection attacks. That's why development teams must integrate automated checks. And who pays the bill when software crashes due to these oversights?

The solution is to turn these checks into mandatory 'gates' within the development cycle. This means AI-generated code cannot proceed to the next phase until it has passed all required tests. It's like an exam: if you don't pass, you don't move forward.

These 'gates' are automated tools that analyze the code. They verify it's secure, performant, and well-written. Only after receiving the green light from these 'guardians' can the AI code be integrated into the final project. This approach ensures AI speed doesn't compromise quality.

Implementing these 'gates' requires an initial investment, sure. But it prevents far more costly problems down the line, such as critical bugs or security flaws that only emerge after product launch. It's an insurance policy on the quality of the software we use daily. Companies adopting AI code generation, such as Google and Microsoft, are investing in CI/CD (Continuous Integration/Continuous Delivery) pipelines with rigorous validation steps to manage code quality.