Benchmarking Software Vulnerability Detection Techniques: A Survey
Yingzhou Bi, Jiangtao Huang, Penghui Liu, Lianmei Wang

TL;DR
This survey comprehensively reviews current benchmarking approaches for software vulnerability detection techniques, highlighting challenges and proposing solutions for evaluating traditional and deep learning-based methods.
Contribution
It is the first survey to systematically analyze and summarize benchmarking practices and challenges for both traditional and deep learning vulnerability detection techniques.
Findings
Identifies key challenges in benchmarking vulnerability detection methods.
Highlights differences between traditional and deep learning approaches.
Suggests potential solutions to improve benchmarking practices.
Abstract
Software vulnerabilities can have serious consequences, which is why many techniques have been proposed to defend against them. Among these, vulnerability detection techniques are a major area of focus. However, there is a lack of a comprehensive approach for benchmarking these proposed techniques. In this paper, we present the first survey that comprehensively investigates and summarizes the current state of software vulnerability detection benchmarking. We review the current literature on benchmarking vulnerability detection, including benchmarking approaches in technique-proposing papers and empirical studies. We also separately discuss the benchmarking approaches for traditional and deep learning-based vulnerability detection techniques. Our survey analyzes the challenges of benchmarking software vulnerability detection techniques and the difficulties involved. We summarize the…
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Taxonomy
TopicsSoftware Engineering Research · Software Reliability and Analysis Research · Web Application Security Vulnerabilities
