WildCode: An Empirical Analysis of Code Generated by ChatGPT
By: Kobra Khanmohammadi, Pooria Roy, Raphael Khoury, Abdelwahab Hamou-Lhadj, Wilfried Patrick Konan
Published: 2025-12-04
View on arXiv →Abstract
This paper presents a large-scale empirical analysis of real-life code generated by ChatGPT, evaluating its correctness and security, and highlighting user's lack of security awareness for LLM-generated code.
Impact
practical
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Topics
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💡 Simple Explanation
Imagine hiring a hyper-fast junior developer who has memorized every textbook but lacks real-world intuition. They write code instantly that looks perfect on the surface, but often misses subtle safety checks or handles rare situations poorly. This paper acts like a senior engineer conducting a massive performance review of this 'AI employee.' The researchers analyzed thousands of code snippets to pinpoint exactly where the AI tends to cut corners, ignore security locks, or hallucinate functionality, providing a safety manual for human managers who want to use this tool without breaking their software.
🔍 Critical Analysis
The paper 'WildCode' provides a necessary empirical grounding for the anecdotal skepticism surrounding AI-generated code. Its strength lies in the large-scale evaluation of syntax versus semantics, highlighting that while ChatGPT excels at 'boilerplate' correctness, it often fails at edge-case logic and security best practices. However, the study faces limitations regarding the 'moving target' problem; the specific version of ChatGPT tested may already be obsolete by the time of publication. Furthermore, the analysis relies heavily on synthetic prompts rather than organically generated code within complex, legacy codebases, which may oversimplify the integration challenges developers face in real-world environments.
💰 Practical Applications
- Development of a static analysis tool specifically tuned to detect 'AI-hallucinated' libraries and security patterns common in LLM code.
- Creation of an educational certification program: 'Auditing AI Code' for senior developers.
- A B2B platform that acts as a middleware firewall, sanitizing and auto-correcting AI code snippets before they enter a corporate repository.
- Consulting services for legal and compliance firms to verify IP risks in AI-generated codebases.
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💬 Discussion (1 comments)
thats interesting one