DCatch

Liu, Haopeng; Li, Guangpu; Lukman, Jeffrey F.; Li, Jiaxin; Lu, Shan; Gunawi, Haryadi S.; Tian, Chen

doi:10.1145/3037697.3037735

Cited by 29 publications

(2 citation statements)

References 26 publications

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“…With a goal of detecting bugs due to the ordering of two events, Bita [Tasharofi et al 2013] covers only the pairwise orderings of the program events. Recent work [Leesatapornwongsa et al 2014;Liu et al 2017;Lukman et al 2019] reduces the state space by using some semantic information about the system under test. Different from the systematic testing approaches with a controlled scheduler, Falcon [Leners et al 2011] and Jepsen [Kingsbury 2018] modify the environment in which the system under test runs and stress test the system to expose faulty behaviors.…”

Section: Related Workmentioning

confidence: 99%

Trace aware random testing for distributed systems

Ozkan

Majumdar

Oraee

2019

Proc. ACM Program. Lang.

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Distributed and concurrent applications often have subtle bugs that only get exposed under specific schedules. While these schedules may be found by systematic model checking techniques, in practice, model checkers do not scale to large systems. On the other hand, naive random exploration techniques often require a very large number of runs to find the specific interactions needed to expose a bug. In recent years, several random testing algorithms have been proposed that, on the one hand, exploit state-space reduction strategies from model checking and, on the other, provide guarantees on the probability of hitting bugs of certain kinds. These existing techniques exploit two orthogonal strategies to reduce the state space: partial-order reduction and bug depth. Testing algorithms based on partial order techniques, such as RAPOS or POS, ensure nonredundant exploration of independent interleavings among system events by imposing an equivalence relation on schedules and ideally exploring only one schedule from each equivalence class. Techniques based on bug depth, such as PCT, exploit the empirical observation that many bugs are exposed by the clever scheduling of a small number of key events. They bias the sample space of schedules to only cover all executions of small depth, rather than the much larger space of all schedules. At this point, there is no random testing algorithm that combines the power of both approaches. In this paper, we provide such an algorithm. Our algorithm, trace-aware PCT (taPCT), extends and unifies several different algorithms in the random testing literature. It samples the space of low-depth executions by constructing a schedule online, while taking dependencies among events into account. Moreover, the algorithm comes with a theoretical guarantee on the probability of sampling a trace of low depthÐthe probability grows exponentially with the depth but only polynomially with the number of racy events explored. We further show that the guarantee is optimal among a large class of techniques. We empirically compare our algorithm with several state-of-the-art random testing approaches for concurrent software on two large-scale distributed systems, Zookeeper and Cassandra, and show that our approach is effective in uncovering subtle bugs and usually outperforms related random testing algorithms. CCS Concepts: • Mathematics of computing → Combinatorics; • Software and its engineering → Software testing and debugging; • Theory of computation → Generating random combinatorial structures.

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Section: Related Workmentioning

confidence: 99%

Trace aware random testing for distributed systems

Ozkan

Majumdar

Oraee

2019

Proc. ACM Program. Lang.

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“…Atomicity allows programmers to reason about atomic blocks without worrying about the effects of other threads. Unfortunately, violation of atomicity specifications is quite common and is the root cause in a majority of real-world bugs [9,13,22,29,35,37,61].…”

Section: Introductionmentioning

confidence: 99%

Atomicity Checking in Linear Time using Vector Clocks

Mathur

Viswanathan

2020

Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Syste

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Multi-threaded programs are challenging to write. Developers often need to reason about a prohibitively large number of thread interleavings to reason about the behavior of software. A non-interference property like atomicity can reduce this interleaving space by ensuring that any execution is equivalent to an execution where all atomic blocks are executed serially. We consider the well studied notion of conflict serializability for dynamically checking atomicity. Existing algorithms detect violations of conflict serializability by detecting cycles in a graph of transactions observed in a given execution. The number of edges in such a graph can grow quadratically with the length of the trace making the analysis not scalable. In this paper, we present AeroDrome, a novel single pass linear time algorithm that uses vector clocks to detect violations of conflict serializability in an online setting. Experiments show that AeroDrome scales to traces with a large number of events with significant speedup. CCS Concepts. • Software and its engineering → Dynamic analysis; Software testing and debugging.

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Understanding and Statically Detecting Synchronization Performance Bugs in Distributed Cloud Systems

Zhang

et al. 2019

IEEE Access

Self Cite

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In such an information society, the Internet of Things (IoT) plays an increasingly important role in our daily lives. With such a huge number of deployed IoT devices, Cyber-Physical System (CPS) calls for powerful distributed infrastructures to supply big data computing, intelligence, and storage services. With the increasingly complex distributed software infrastructures, new intricate bugs continue to manifest, causing huge economic loss. Synchronization performance problems, which means that improper synchronizations may degrade the performance and even lead to service exception, heavily influence the entire distributed cluster, imperiling the reliability of the system. As one kind of performance problems, the synchronization performance problems are acknowledged as difficult to diagnosis and fix. We collect 26 performance issues in three real-world distributed systems: HDFS, Hadoop MapReduce, and HBase, and do analysis on their root cause, fix strategy, and algorithm complexity in order to understand these synchronization performance bugs better. Then, we implement a static detection tool including critical section identifier, loop identifier, inner loop identifier, expensive loop identifier, and pruning component. After that, we evaluate our detection tool on these three distributed systems with sampled bugs. In the evaluation, our detection tool accurately finds out all the target bugs. Besides, it points out more new potential performance problems than the previous works. With the strict performance overhead, our detection tool is proved to be greatly efficient. INDEX TERMS Software debugging, software performance, distributed computing, software reliability.

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DCatch

Cited by 29 publications

References 26 publications

Trace aware random testing for distributed systems

Trace aware random testing for distributed systems

Atomicity Checking in Linear Time using Vector Clocks

Understanding and Statically Detecting Synchronization Performance Bugs in Distributed Cloud Systems

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