Navigating the Concurrency Landscape: A Survey of Race Condition Vulnerability Detectors

As technology continues to advance and we usher in the era of Industry 5.0, there has been a profound paradigm shift in operating systems, file systems, web, and network applications. The conventional utilization of multiprocessing and multicore systems has made concurrent programming increasingly pervasive. However, this transformation has brought about a new set of issues known as concurrency bugs, which, due to their wide prevalence in concurrent programs, have led to severe failures and potential security exploits. Over the past two decades, numerous researchers have dedicated their efforts to unveiling, detecting, mitigating, and preventing these bugs, with the last decade witnessing a surge in research within this domain. Among the spectrum of concurrency bugs, data races or race condition vulnerabilities stand out as the most prevalent, accounting for a staggering 80\% of all concurrency bugs. This survey paper is focused on the realm of race condition bug detectors. We systematically categorize these detectors based on the diverse methodologies they employ. Additionally, we delve into the techniques and algorithms associated with race detection, tracing the evolution of this field over time. Furthermore, we shed light on the application of fuzzing techniques in the detection of race condition vulnerabilities. By reviewing these detectors and their static analyses, we draw conclusions and outline potential future research directions, including enhancing accuracy, performance, applicability, and comprehensiveness in race condition vulnerability detection.