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ICSE 2020
Wed 24 June - Thu 16 July 2020
Thu 9 Jul 2020 08:05 - 08:17 at Baekje - I16-Testing and Debugging 2 Chair(s): Rui Abreu

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 Jul
Times are displayed in time zone: (UTC) Coordinated Universal Time change

08:05 - 09:05
I16-Testing and Debugging 2Technical Papers / Journal First at Baekje
Chair(s): Rui AbreuInstituto Superior Técnico, U. Lisboa & INESC-ID
08:05
12m
Talk
Low-Overhead Deadlock PredictionTechnical
Technical Papers
Yan CaiInstitute of Software, Chinese Academy of Sciences, Ruijie MengUniversity of Chinese Academy of Sciences, Jens PalsbergUniversity of California, Los Angeles
08:17
8m
Talk
The Impact of Feature Reduction Techniques on Defect Prediction ModelsJ1
Journal First
Masanari KondoKyoto Institute of Technology, Cor-Paul BezemerUniversity of Alberta, Canada, Yasutaka KameiKyushu University, Ahmed E. HassanQueen's University, Osamu MizunoKyoto Institute of Technology
08:25
8m
Talk
The Impact of Correlated Metrics on the Interpretation of Defect ModelsJ1
Journal First
Jirayus JiarpakdeeMonash University, Australia, Chakkrit TantithamthavornMonash University, Australia, Ahmed E. HassanQueen's University
08:33
8m
Talk
The Impact of Mislabeled Changes by SZZ on Just-in-Time Defect PredictionJ1
Journal First
Yuanrui FanZhejiang University, Xin XiaMonash University, Daniel Alencar Da CostaUniversity of Otago, David LoSingapore Management University, Ahmed E. HassanQueen's University, Shanping LiZhejiang University
08:41
8m
Talk
Which Variables Should I Log?J1
Journal First
Zhongxin LiuZhejiang University, Xin XiaMonash University, David LoSingapore Management University, Zhenchang XingAustralia National University, Ahmed E. HassanQueen's University, Shanping LiZhejiang University
08:49
12m
Talk
Understanding the Automated Parameter Optimization on Transfer Learning for Cross-Project Defect Prediction: An Empirical StudyTechnicalArtifact Available
Technical Papers
Ke LiUniversity of Exeter, Zilin XiangUniversity of Electronic Science and Technology of China, Tao ChenLoughborough University, Shuo Wang, Kay Chen TanCity University of Hong Kong
Pre-print