Multithreaded programs can have deadlocks, even after deployment, so users may want to run deadlock tools on deployed programs. However, current deadlock predictors such as MagicLock and UnDead have large overheads that make them impractical for end-user deployment and confine their use to development time. Such overhead stems from running an exponential-time algorithm on a large execution trace. In this paper, we present the first low-overhead deadlock predictor, called AirLock, that is fit for both in-house testing and deployed programs. AirLock maintains a small predictive lock reachability graph, searches the graph for cycles, and runs an exponential-time algorithm only for each cycle. This approach lets AirLock find the same deadlocks as MagicLock and UnDead but with much less overhead because the number of cycles is small in practice. Our experiments with real-world benchmarks show that the average time overhead of AirLock is 3.5%, which is three orders of magnitude less than that of MagicLock and UnDead. AirLock’s low overhead makes it suitable for use with fuzz testers like AFL and on-the-fly after deployment.
Thu 9 JulDisplayed time zone: (UTC) Coordinated Universal Time change
08:05 - 09:05 | I16-Testing and Debugging 2Technical Papers / Journal First at Baekje Chair(s): Rui Abreu Instituto Superior Técnico, U. Lisboa & INESC-ID | ||
08:05 12mTalk | Low-Overhead Deadlock PredictionTechnical Technical Papers Yan Cai Institute of Software, Chinese Academy of Sciences, Ruijie Meng University of Chinese Academy of Sciences, Jens Palsberg University of California, Los Angeles | ||
08:17 8mTalk | The Impact of Feature Reduction Techniques on Defect Prediction ModelsJ1 Journal First Masanari Kondo Kyoto Institute of Technology, Cor-Paul Bezemer University of Alberta, Canada, Yasutaka Kamei Kyushu University, Ahmed E. Hassan Queen's University, Osamu Mizuno Kyoto Institute of Technology | ||
08:25 8mTalk | The Impact of Correlated Metrics on the Interpretation of Defect ModelsJ1 Journal First Jirayus Jiarpakdee Monash University, Australia, Kla Tantithamthavorn Monash University, Australia, Ahmed E. Hassan Queen's University | ||
08:33 8mTalk | The Impact of Mislabeled Changes by SZZ on Just-in-Time Defect PredictionJ1 Journal First Yuanrui Fan Zhejiang University, Xin Xia Monash University, Daniel Alencar Da Costa University of Otago, David Lo Singapore Management University, Ahmed E. Hassan Queen's University, Shanping Li Zhejiang University | ||
08:41 8mTalk | Which Variables Should I Log?J1 Journal First Zhongxin Liu Zhejiang University, Xin Xia Monash University, David Lo Singapore Management University, Zhenchang Xing Australia National University, Ahmed E. Hassan Queen's University, Shanping Li Zhejiang University | ||
08:49 12mTalk | Understanding the Automated Parameter Optimization on Transfer Learning for Cross-Project Defect Prediction: An Empirical StudyTechnical Technical Papers Ke Li University of Exeter, Zilin Xiang University of Electronic Science and Technology of China, Tao Chen Loughborough University, Shuo Wang , Kay Chen Tan City University of Hong Kong Pre-print | ||