Lightweight Method for On-the-fly Detection of Multivariable Atomicity Violations (Best Paper)
Testing to detect on-the-fly concurrency errors, such as atomicity violations caused by multiple shared variables, in a multi-threaded program is challenging because of the need to consider various factors, including correlations between variables and access event interleaving. An improved method for detecting atomicity violations during program execution is presented in this study. This method applies a straightforward approach to recognize a substantial number of related variables that make up atomicity as a single group and as one variable. This enhanced method was implemented, similarly to a test tool, and experimental comparisons were performed using a set of synthetic programs that modeled the execution of the seven representative multivariable atomicity violations. The results showed that compared with the original run of the test programs and a state-of-the-art detection method, the execution time increased by 1.07 and 1.05, respectively. This result included the accuracy of detecting all the multivariable atomicity violations undetected by the prior method.
Sun 16 AprDisplayed time zone: Dublin change
11:00 - 12:30 | |||
11:00 30mTalk | Preliminary results in using attention for increasing attack identification efficiency ITEQS Tanwir Ahmad Åbo Akademi University, Dragos Truscan Åbo Akademi University, Jüri Vain Tallinn University of Technology, Estonia | ||
11:30 30mTalk | Lightweight Method for On-the-fly Detection of Multivariable Atomicity Violations (Best Paper) ITEQS Changhui Bae Gyeongsang National University, Euteum Choi Gyeongsang National Unviersity, Yong-Kee Jun Gyeongsang National University, Ok-Kyoon Ha Kyungwoon University | ||
12:00 30mTalk | Using Assurance Cases to assure the fulfillment of non-functional requirements of AI-based systems - Lessons learned ITEQS Marc Hauer TU Kaiserslautern - Algorithm Accountability Lab , Lena Müller-Kress winnovation consulting gmbh, Gertraud Leimüller winnovation consulting gmbh & leiwand.ai gmbh, Katharina Zweig TU Kaiserslautern - Algorithm Accountability Lab |