Qodana / Static Code Analysis Guide / Code Analysis for AI-Generated Code
You've probably heard the buzz around AI coding assistants. Perhaps you're even already using Junie, ChatGPT, or Claude to help write your functions.
While AI-generated code has its merits, we’ve seen enough security breaches and industry horror stories to know it’s not a standalone solution. Whether you're crushing it at work or building side projects in your spare time, understanding AI code analysis can help you manage risk and improve your code quality.
AI-generated code is code written by artificial intelligence models instead of humans. These models have been trained using millions of lines of code from repositories, documentation, and programming forums, including:
When you give these AI models a prompt like "write a function to sort an array", they don't really understand what sorting means. Instead, they recognize patterns from their training data and write code that statistically resembles what a human might write.
Imagine you had to write a poem, but you’ve never studied anything about poetry. You might look at dozens of poems, notice patterns in word placement and structure, and then try to match what you’ve learned. That's essentially what AI does with code, but in a more turbo-charged, pattern-matching way.
The key difference is that AI doesn't understand your specific project context, your team's coding standards, or the subtle business logic of your application. It's generating code based on general patterns, not your needs. And it won’t have the creative insight only a human can provide.
Let’s look at the advantages and potential pitfalls of using AI when you’re coding.
According to a 2024 study by GitHub, developers using GitHub Copilot had a 53% greater chance of passing all 10 tests in the study. It wrote code that was more useful, easier to read, and quicker to pass the approval stage. That's a serious productivity boost, especially when you're learning and every small win counts.
Perhaps the biggest problem with AI-generated code is that some people think it’s better than human-written code. While the logic might not check out, the perception that robots are flawless and humans are inherently flawed sometimes causes confusion. For example, research from the Center for Security and Emerging Technology at Georgetown University shows that developers believe AI-generated code is more secure. This means users may put unwarranted levels of trust in AI-generated code and overlook careful code reviews.
Common risks for AI-generated code include:
Here's a comparison that might help:
Understands your project
Pattern-matches from training data
Follows team conventions
Uses generic best practices
Considers long-term maintenance
Focuses on immediate functionality
Takes longer to write
Extremely fast generation
Fewer security blind spots
May include common vulnerabilities
Variable quality, depending on the developer
Consistently "good enough" quality
This is where things get interesting. Static analysis becomes even more critical when you're dealing with AI-generated code.
Think of static analysis as your code's health checkup. Just like you'd get a physical exam even if you feel fine, you want to scan your code for potential issues before they become real problems, if they aren’t already.
Security first
You need to pay extra attention to possible security issues in AI-generated code. AI models were trained on code from the wild internet, including repositories with known vulnerabilities. They might include those same security flaws in your project.
JetBrains Qodana can catch many of these issues automatically. It checks for things like:
For example, if you ask AI to create a login function, it might generate something like this bit of
Python code:
def login(username, password):
query = f"SELECT * FROM users WHERE username='{username}' AND password='{password}'"
result = db.execute(query)
return result.fetchone() is not NoneThis is a textbook SQL injection vulnerability. A user up to no good could destroy your database by inputting this as their username:
admin'; DROP TABLE users; --
A good static analysis tool will warn you about this and recommend using parameterized queries instead.
AI-generated code doesn’t understand how to maintain your application the same way an experienced developer does. Static analysis helps catch problems like:
Here's where JetBrains's CI/CD code analysis tools really shine. You can set up Qodana to automatically scan every pull request that includes AI-generated code to find AI code risks. If AI suggests something that could cause a problem, you'll know about it and be able to fix the bug before it hits production.
The workflow looks like this:
This workflow creates a safety net and is particularly valuable for developers who might not have the experience necessary to spot subtle problems in AI-generated code.
Before adding AI-generated code, run static analysis on your existing codebase. This helps you understand your current quality metrics and avoid introducing regressions.
Not all AI code risks are the same. Authentication, data processing, and external APIs need extra attention.
Different static code analysis tools catch different types of issues. Qodana provides good coverage, but you might also want to employ security-focused scanners.
AI doesn't understand your specific use case. Even if the generated code passes static code analysis, ask yourself if it fits your architecture and follows your team's patterns.
Let's say you're building a web app and ask AI to create a file upload function. AI might generate something that works perfectly in testing but would have serious security problems in production.
A tool like Qodana can catch problems like:
These aren't necessarily bugs that would crash your app, but they could create serious security vulnerabilities that manual testing might miss.
AI-generated code is incredibly powerful, but it's not magic. AI is great at following patterns and cranking out code quickly, but it still needs experienced oversight to catch more subtle issues.
This is why it’s crucial that you build analysis into your workflow from day one. Whether you're using JetBrains' comprehensive Qodana platform or cobbling together your own toolkit, the important thing is making sure static analysis is part of your normal way of working.
For developers just starting out, this might seem like overkill. You might ask yourself, "Why not test the code and see if it works?" Problems are much more expensive to fix by the time you find security vulnerabilities or performance issues during testing. Static analysis catches problems early, when they're still cheap and easy to resolve.
As AI coding tools become more mature, the gap between AI-generated and human-written code will probably shrink. For now, treating AI as a powerful assistant rather than a replacement for careful software development practices is the smart play.
Your future self, and your colleagues, will thank you for taking the time to properly analyze that AI-generated code before shipping it to production.
Check out the Qodana blog for more advice and tips on improving your code quality.