Towards Better Software Quality in the Era of Large Language Models | Lingming Zhang (UIUC)
AI Summary
Summary of the Video: Improving Software Quality with Large Language Models
Introduction
- Presentation on utilizing Large Language Models (LLMs) to enhance software quality.
- The prevalence of software in daily life leads to bugs that can have significant consequences.
Software Development Cycle
- Simplified cycle:
- Code generation by developers
- Bug detection and fixing
- This cycle can be repetitive and resource-intensive, especially for large companies.
Research on Automation
- Research has aimed to automate various development tasks:
- Code synthesis
- Testing
- Bug repair
- The focus is on the impact of LLMs on these tasks.
Background on LLMs
- LLMs predict token sequences based on probabilities, built on Transformer architecture.
- They have large parameter counts and are trained on vast datasets, enhancing their functionality in coding tasks.
LLMs in Software Quality Improvement
- Testing:
- Emphasis on fuzz testing as a method of generating inputs to uncover bugs.
- Projects like OSS-Fuzz have successfully identified vulnerabilities.
- Proposed focus on fuzz testing for deep libraries critical for applications.
- Automatic Program Repair:
- LLMs can directly generate fixes rather than translate buggy code to bug-free code.
- Utilizing contextual information for generating patches without extensive bug-fixing datasets.
- Program Synthesis:
- Discussed the shortcomings of existing models when using widely accepted benchmarks such as human-eval.
- Proposed a dataset called human-evo-plus to evaluate LLM performance more rigorously.
Conclusion
- The effectiveness of LLMs in improving software quality is promising, with the capability to tackle significant challenges in software development tasks.