Why You Need an AI Code Checker

Imagine waking up to an email from a junior developer: I just wrote 1,000 lines of flawless code in under an hour! Impressive? Absolutely. Suspicious? Maybe.

Thanks to AI tools like GitHub Copilot, ChatGPT, Cursor and Claude, writing code has never been faster. But as AI-generated code floods repositories, classrooms, and businesses, a critical question arises: How do we know if code was written by a human or an AI? That’s where AI code checkers come in tools designed to detect whether a piece of code was crafted by human hands or machine algorithms.

In this blog, we’ll explore how an AI checker for code works, why it matters, and how you can check code for AI traces.

What Actually is Code Checkers?

A code detector or code checker is a tool that analyzes and evaluates source code for various factors such as quality, syntax errors, security vulnerabilities, and adherence to best practices. These tools can be used to:

1. Detect AI-generated code: Some detectors specifically look for patterns or characteristics common in code written by AI models, like excessive comments, repetitive patterns, or unusual structures that might not be typical of human-written code.

2. Check for errors: Detecting bugs, syntax errors, or logical mistakes in the code.

3. Identify vulnerabilities: Analyzing the code for potential security flaws or areas where the code could be exploited by attackers.

4. Enforce coding standards: Checking if the code adheres to predefined coding guidelines or best practices, which might include naming conventions, formatting, and code complexity.

5. Analyze performance: Assessing the efficiency of the code in terms of speed, memory usage, and scalability.

Why Should You Care About an AI-Generated Code?

We’re not in the 90s or even 2020 – it’s 2025 now. Even 10th-grade students are writing code, literally doing vibe coding. But there’s some confusion these days. There’s code written before ChatGPT came along, and a lot of code generated after it. But how do you know if the code is AI-generated, and why should you care about it?

AI-generated code isn’t inherently bad—it’s a powerful assistant. But unchecked reliance on it can lead to:

1. Plagiarism & Licensing Risks

• AI models like GitHub Copilot sometimes reproduce open-source code verbatim, leading to licensing violations.

• A 2023 study found that 40% of AI-suggested code snippets matched existing copyrighted code without attribution.

2. Security Vulnerabilities

• AI doesn’t always understand security best practices.

• Researchers found that AI-generated code often contains hidden vulnerabilities, like SQL injection flaws.

• Every AI model has its own knowledge cutoff date, so these AI models don’t have data on recent vulnerability fixes or patches. This means there’s a chance they might generate vulnerable code.

3. Lack of Original Problem-Solving

• Relying too much on AI can stunt a developer’s growth like using a calculator before learning basic math.

• In coding interviews, candidates might submit AI-generated solutions without truly understanding them.

4. The AI Hallucination Problem

• Sometimes, AI generates convincing but completely wrong code.

• Imagine deploying an AI-written function that looks correct but fails in production!

Bottom line: AI is a great co-pilot, but human oversight is non-negotiable.

We saw the problems with AI-generated code. Now, let’s see how AI code checkers work.

How Do AI Code Checkers Work?

Detecting AI-generated code isn’t magic—it’s pattern recognition. Here’s how these tools analyze code to spot AI fingerprints:

1. Analyzing Code Structure & Repetition

• AI-generated code tends to follow predictable, formulaic patterns.

• Human-written code has more variability quirks, personal style, and occasional inefficiencies.

2. Checking Comments & Documentation

• Humans write contextual comments explaining why they did something.

• AI often generates generic or missing comments (e.g., "This function calculates the result.").

3. Evaluating Complexity & Creativity

• AI tends to produce overly optimized or simplistic solutions.

• Humans sometimes write "ugly but clever" workarounds.

4. Metadata & Tool Fingerprints

• Some AI tools leave subtle signatures in formatting or variable naming.

• Example: GitHub Copilot sometimes adds “#Generated by Copilot” in comments.

5. Stylometric Analysis (Like a Code "Handwriting" Test)

• Just as forensic experts analyze writing style, AI detectors look for:

  • Variable naming habits (Do they follow a consistent convention?)

  • Indentation quirks (Humans have personal preferences; AI follows strict rules.)

Top AI Code Checker Tools in 2025

Want to check if code is AI-generated? Here are the best tools available:

Tip: No tool is 100% accurate—always combine AI detection with manual review.

Let see how Code Detector from Synk:

You can import any of your projects from GitHub, GitLab, or Bitbucket, and Snyk can identify vulnerabilities, best practices related to security, and provide a security score. I’m not sure if they are using AI for this, but it is a good and useful tool if you are really concerned about security.

The above is the dashboard of Snyk. You can import both private and public repositories and try it out.

How to Manually Check for AI-Generated Code

Even without tools, without paying for subscriptions, you can spot AI-written code with these tricks:

1. Look for "Too Perfect" Code

• AI-generated code is often flawlessly structured but lacks nuance.

• Example: A sorting algorithm with zero comments or edge-case handling.

2. Test for Understanding

• Ask the developer: Can you explain why you used this approach?

• If they struggle, it might be AI-generated. Just simple as that.

3. Check for Unusual Variable Names

• AI sometimes generates random or overly literal names (e.g., temp_var_1).

• Humans use meaningful names (e.g., userInputBuffer).

4. Run Static Analysis Tools

• Tools like SonarQube or ESLint can flag suspicious patterns.

Guess what, even with these, you can’t confidently say whether the code is generated by an AI or not. You can just make a prediction. For a small example, if there are a lot of comments in the code, you might guess it’s generated by an AI, because humans don’t usually write as many comments as AI does.

New AI Testing Agent in the Town:

Tired of using ChatGPT or Copilot a lot? What if I told you there’s an alternative to both? You can use Keploy – an AI-powered unit test generator, right in your VS Code or in GitHub as an agent.

How cool is this? Testing directly in PRs – so developers don’t need to write test cases for their new features. Keploy writes them for you! No noisy stuff – just clean, focused tests targeting the code changes. And yes, it’s HIPAA-compliant too.

Just remember, we’re in 2025, not 2018 or 2019. So, let’s start testing like it’s 2025.

What are you waiting for? Like how you’re using AI for coding, use AI for testing too with Keploy. Just give it a try!

Links to VScode Extension: https://marketplace.visualstudio.com/items?itemName=Keploy.keployio

Links to Github PR agent: https://github.com/apps/keploy

Conclusion:

AI-generated code is here to stay, and that’s not a bad thing. AI models are going to become even more advanced in the coming days. But like any powerful tool, it requires responsible use.

Before you deploy that AI-written function, ask yourself:

• Do I understand how it works?

• Have I tested it thoroughly?

• Could an AI code checker flag this?

The future belongs to developers who harness AI’s power without losing their human edge.

Some of the other Useful blogs for your reference:

1. How AI is transforming Software and Testing- https://keploy.io/blog/community/how-ai-is-transforming-software-and-testing-annotations

2. Top 5 AI tools in 2025 – https://keploy.io/blog/community/top-5-ai-tools-in-2025-developer-should-must-try

3. Best free ai code generators- https://keploy.io/blog/community/best-free-ai-code-generators

4. AI powered test automation- https://keploy.io/blog/community/ai-powered-test-automation

5. AI testing prompt engineering – https://keploy.io/blog/community/ai-testing-prompt-engineering

FAQs:

1. Can AI code checkers detect all AI-generated code?

No tool is perfect, but advanced models (like CodeBERT) achieve 90%+ accuracy.

2. Is using AI to write code cheating?

Not inherently just like using a calculator isn’t cheating. But submitting AI code as your own without understanding it is unethical.

3. Will AI replace human developers?

Unlikely AI is a tool, not a replacement. The best developers will use AI to enhance, not replace, their skills.

4. Can we trust the code generated by AI?

Yes, you can trust the code, but at the same time, you need to validate it yourself because sometimes AI models hallucinate.

5. is Keploy PR Agent free to use?

Yes, you can use Keploy PR Agent for free. To get access for more repositories, see their pricing page https://keploy.io/pricing.