RDEL #58: What are the most common bugs in LLM-generated code?
This week, we discover the different types of bugs that LLM-generated code can provide, and how to mitigate their potential impact.
Welcome back to Research-Driven Engineering Leadership. Each week, we pose an interesting topic in engineering leadership and apply the latest research in the field to drive to an answer.
By now, most software engineering teams are using some form of LLM to augment their code generation. This week, ask: How prevalent and problematic are bugs in code generated by Large Language Models (LLMs), and what can engineering leaders do to mitigate these risks?
The context
The rise of AI-driven code generation tools like GitHub Copilot have revolutionized the way developers write code, offering the promise of increased productivity and automation. But while these tools can generate code snippets quickly and efficiently, they are not infallible. Just like human developers, LLMs are prone to errors, and the bugs they produce can be quite different from those made by humans.
As these tools become more integrated into development workflows, it’s crucial for engineering leaders to understand the unique challenges these tools present. As reliance increases and trust varies (which we covered here), engineering teams need to consider how LLM-generated bugs might change their development and testing workflows. The errors introduced by LLMs can be subtle, potentially introducing faults that may not be immediately obvious but could have significant impacts on the functionality and security of software.
The research
Researchers examined 333 bugs from code generated by three leading LLMs: CodeGen, PanGu-Coder, and Codex. The researchers identified ten distinct types of bugs that frequently occur in LLM-generated code, which they organized into a taxonomy.
The most common bug patterns included
Misinterpretations (20.77%): where the LLM-generated code deviates from the intended functionality described in the prompt
Missing Corner Cases (15.27%): where the LLM produces code that works for typical inputs but fails to account for less common or edge cases.
Hallucinated Objects (9.57%): where LLMs generate code that references objects or functions that do not exist, leading to execution errors.
Analyzing the taxonomy and frequency of various LLM-generated bugs, the researchers noted a few unique behaviors of these bugs:
Some bug patterns were distinct to an LLM, and not common human errors: Certain bug patterns, such as "Hallucinated Objects" and "Silly Mistakes," are not commonly observed in human-written code. These types of errors, such as calling undefined functions or generating redundant code conditions, are less likely to occur with human developers, particularly because they are often easily caught by IDEs and linters.
Types of bugs varied across LLMs: The prevalence of specific bug patterns varied significantly across different LLMs. For example, Codex was more likely to generate bugs related to "Non-Prompted Consideration," whereas CodeGen and PanGu-Coder had higher occurrences of "Misinterpretations."
Prompt clarity impacted the likelihood of generating bugs: The clarity and completeness of the prompt provided to the LLM significantly impacted the likelihood of generating bugs. Ambiguous or incomplete prompts led to a higher incidence of misinterpretations and missing corner cases.
The application
LLM-introduced bugs can introduce subtle but critical flaws into software, potentially leading to functional issues, security vulnerabilities, and increased technical debt. As LLMs become more integrated into development workflows, understanding and mitigating these unique risks is essential for maintaining software quality and reliability.
Engineering leaders can apply these lessons to their teams in the following ways:
Implement Rigorous Testing Frameworks: Develop a more thoughtful testing practices specifically designed to identify and address the unique bug patterns found in LLM-generated code, such as missing corner cases and hallucinated objects. Where possible, automate these practices as part of the build process so they can be caught earlier in the development pipeline.
Enhance Code Review Processes: Encourage thorough code reviews of LLM-generated code, treating it as though it were produced by a junior developer. Pair programming and collaborative review sessions can help catch subtle errors that may be overlooked by individual developers.
Provide Training: Educate teams on the specific risks and limitations of using LLMs for code generation. Teams can also create best practices around prompt generation to minimize the frequency and impact of certain LLM-generated mistakes.
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As LLMs continue to improve the pace of code generation, we hope this edition helps your teams maintain high-quality, bug-free features in this new development paradigm. Happy Research Monday!
Lizzie