Static analyzers are vital to ensure software quality, but often produce false alarms. In this paper, we focus on the challenging task, directly refining defective static detection rules in the analyzer with Large Language Models to mitigate false positives/negatives fundamentally. This paper introduces RuleRefiner, a novel multi-stage framework for static analyzer rule refinement. Specifically, RuleRefiner systematically employs LLMs by integrating dynamic profiling information for fact-based rule-code alignment, performing differential fault localization to accurately pinpoint error sources, and utilizing targeted templates to guide and constrain LLM-based modifications for precise and minimally disruptive enhancements. Evaluated on 218 real-world refinement tasks, RuleRefiner achieved a pass@5 score of 80.28%, significantly outperforming all selected LLM-based baselines under the same settings. Moreover, the rules refined by RuleRefiner demonstrated high generalization capability comparable to those written by human experts.

Automatic testing has garnered significant attention and success over the past few decades. Techniques such as unit testing and coverage-guided fuzzing have revealed numerous critical software bugs and vulnerabilities. However, a long-standing, formidable challenge for existing techniques is how to achieve higher testing coverage. Constraint-based techniques, such as symbolic execution and concolic testing, have been well-explored and integrated into the existing approaches. With the popularity of Large Language Models (LLMs), recent research efforts to design tailored prompts to generate inputs that can reach more uncovered target branches. However, the effectiveness of using LLMs for generating such directed inputs and the comparison with the proven constraint-based solutions has not been systematically explored.

The Go language (Go/Golang) has been attracting increasing attention from the industry over recent years due to its strong concurrency support and ease of deployment. This programming language encourages developers to use channel-based concurrency, which simplifies the development of concurrent programs. Unfortunately, it also introduces new concurrency problems that differ from those caused by the mechanism of shared memory concurrency. However, there are only few works that aim to detect such Go-specific concurrency issues. Even state-of-the-art testing tools will miss critical concurrent bugs that require fine-grained and effective interleaving exploration. This paper presents GoPie, a novel testing approach for detecting Go concurrency bugs through primitive-constrained interleaving exploration. GoPie utilizes execution histories to identify new interleavings instead of relying on exhaustive exploration or random scheduling. To evaluate its performance, we applied GoPie to existing benchmarks and large-scale open-source projects. Results show that GoPie can effectively explore concurrent interleavings and detect significantly more bugs in the benchmark. Furthermore, it uncovered 11 unique previously unknown concurrent bugs, and 9 of which have been confirmed.