Concetti Chiave
Automatically generated code from Large Language Models (LLMs) faces significant challenges around quality, security, privacy, and trustworthiness that need to be addressed before widespread integration into software projects.
Sintesi
The article discusses the challenges and opportunities in integrating code generated by Large Language Models (LLMs) into software projects. It highlights the key issues around the trustworthiness of LLM-generated code, including:
Quality Concerns:
LLMs tend to produce low-quality code with functional, performance, and security issues.
Existing studies focus on evaluating functional suitability, usability, reliability, security, and maintainability, but more work is needed on compatibility and portability.
Potential solutions include fusing all quality characteristics into LLMs or prioritizing certain characteristics for specific applications.
Trustworthiness Attributes:
Code-specific attributes: Security, Reliability, Privacy
Model-specific attributes: Explainability, Robustness, Consistency, Fairness, Ethics
Potential Remedies:
Use high-quality training data for LLMs to minimize vulnerabilities and errors.
Integrate automated checks for security, privacy, and reliability as part of the development process.
Develop techniques for explaining the reasoning behind LLM-generated code.
Ensure LLMs maintain consistent and reproducible outputs.
Address fairness and ethical concerns in the code generated by LLMs.
The article concludes that addressing these trustworthiness challenges is crucial for the widespread adoption of LLM-generated code in real-world software projects.
Statistiche
The code generated by LLMs tends to have a high rate of security vulnerabilities, with around 40% of the generated code found to be vulnerable to exploitation.
LLMs produce verbatim single statement bugs up to twice as often as known, for verbatim correct code.
Citazioni
"Given that LLMs are trained on a vast amount of open-source code corpus, it is likely that the pre-trained code corpus contains unverified code that contain security vulnerabilities. The LLMs learn from such vulnerable and exploitable examples."
"LLMs tend to produce subtle trivial bugs as well. In fact, Jesse et al. [63] reported that Codex and other LLMs produce verbatim single statement bugs up to twice as often as known, for verbatim correct code."