February 21, 2024 at 01:40AM
Large language models (LLMs) show potential in speeding up software development by detecting and addressing common bugs. Google’s Gemini LLM can fix 15% of bugs found using dynamic application security testing (DAST), helping prioritize vulnerabilities often overlooked by developers. AI-powered bug-fixing systems are crucial as machine learning models produce more code and vulnerabilities, offering potential for efficient software fixes and patch application.
From the meeting notes, the key takeaways are:
1. Large language models (LLMs) have shown modest success in generating fixes for specific classes of vulnerabilities, such as uninitialized values, simultaneous data access violations, and buffer overflows.
2. Google’s Gemini LLM can fix 15% of bugs found using dynamic application security testing (DAST) technique, offering a significant efficiency gain for dealing with thousands of vulnerabilities not prioritized by developers.
3. Google’s approach focuses on fixing vulnerabilities found using sanitizers—dynamic application security tools—after code has been committed, but before the application is released to production.
4. The AI not only suggests patches but also enables automated testing of the patch candidates, filtering out invalid patches and ensuring that the fixed code runs properly.
5. AI-based systems for automated bug-fixing will be increasingly important as AI tools lead to the production of more code and potential vulnerabilities in the future.
6. The applications of AI/ML models extend beyond development to IT operations, where they could help create and apply patches to systems, addressing concerns about adverse side effects from patching.
These takeaways highlight the potential of AI-driven bug fixing and patching in improving software development efficiency and addressing existing vulnerabilities.