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Xianfeng Lu

dblp:282/8691 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Software testing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing › fuzzing
fuzz driver generation
0.812024
Applying Fuzz Driver Generation to Native C/C++ Libraries of OEM Android Framework: Obstacles and Solutions · ASE 2024
Software testing
fuzzing
0.812024
Applying Fuzz Driver Generation to Native C/C++ Libraries of OEM Android Framework: Obstacles and Solutions · ASE 2024
Software testing › fuzzing
library fuzzing
0.212024
Applying Fuzz Driver Generation to Native C/C++ Libraries of OEM Android Framework: Obstacles and Solutions · ASE 2024

Methods — techniques the papers use, named apart from their topics

static analysis · 0.8fuzz driver ranking · 0.8
YearPublicationVenuePosition
2024 Applying Fuzz Driver Generation to Native C/C++ Libraries of OEM Android Framework: Obstacles and Solutions
abstract
Fuzz driver generation (FDG) is a fundamental technique for fuzzing library software. Existing FDG approaches have been highly successful with open-source libraries. However, in practice, due to the complex nature of OEM Android frameworks (e.g., customized compilation toolchains, extensive codebases, diverse C/C++ language features), it is not straightforward to integrate existing fuzz driver generation tools with OEM Android libraries. To address this challenge, we first systematically summarize the obstacles to applying existing tools (e.g., FuzzGen) to libraries of an OEM Android (i.e., ColorOS), including compatibility, usability, and effectiveness issues. Following this, we developed a new fuzz driver generation tool, namely FuzzGen++, specifically designed to tackle these obstacles one by one. In our evaluation, we demonstrate the advantages of FuzzGen++ in real-world OEM Android frameworks. FuzzGen++ is compatible with OEM Android and can generate fuzz drivers for all its libraries which are not supported by existing works. The additional analysis of the OEM Android code also enhances its usability within the system. Overall, FuzzGen++ has helped automatically generate 21,457 fuzz drivers. Additionally, through fuzz driver ranking and selection solution, FuzzGen++ figured out cut off 95% fuzz drivers which are less useful. FuzzGen++ supports sophisticated C/C++ features in code analysis, ensuring effectiveness. Compared to hand-written fuzz drivers, FuzzGen++ could generate and select fuzz drivers providing a 107.92% coverage improvement. Furthermore, they discovered 6 bugs, showcasing the capability of FuzzGen++ to find real-world issues.
Shiyan Peng, Yuan Zhang 0009, Jiarun Dai, Zhuoxiang Shen, Lin Wang 0042, Lei Ai, Xianfeng Lu, Min Yang 0002
ASE11