MATUS: Precise Bug Detection via Feature Slice Matching
Analysis
This paper introduces MATUS, a novel approach for bug detection that focuses on mitigating noise interference by extracting and comparing feature slices related to potential bug logic. The key innovation lies in guiding target slicing using prior knowledge from buggy code, enabling more precise bug detection. The successful identification of 31 unknown bugs in the Linux kernel, with 11 assigned CVEs, strongly validates the effectiveness of the proposed method.
Key Takeaways
- •MATUS addresses the problem of noise interference in bug detection by focusing on relevant feature slices.
- •The method uses prior knowledge from buggy code to guide target slicing, improving precision.
- •The approach has demonstrated significant success in identifying real-world bugs in the Linux kernel.
- •The results include confirmed bugs and assigned CVEs, indicating practical impact.
Reference
“MATUS has spotted 31 unknown bugs in the Linux kernel. All of them have been confirmed by the kernel developers, and 11 have been assigned CVEs.”