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PEDAL: a dynamic analysis tool for efficient concurrency bug reproduction in big data environment

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Abstract

Concurrency bugs usually manifest under very rare conditions, and reproducing such bugs can be a challenging task. To reproduce concurrency bugs with a given input, one would have to explore the vast interleaving space, searching for erroneous schedules. The challenges are compounded in a big data environment. This paper explores the topic of concurrency bug reproduction using runtime data. We approach the concurrency testing and bug reproduction problem differently from existing literature, by emphasizing on the preemptable synchronization points. In our approach, a light-weight profiler is implemented to monitor program runs, and collect synchronization points where thread scheduler could intervene and make scheduling decisions. Traces containing important synchronization API calls and shared memory accesses are recorded and analyzed. Based on the preemptable synchronization points, we build a reduced preemption set (RPS) to narrow down the search space for erroneous schedules. We implement an optimized preemption-bounded schedule search algorithm and an RPS directed search algorithm, in order to reproduce concurrency bugs more efficiently. Those schedule exploration algorithms are integrated into our prototype, Profile directed Event driven Dynamic AnaLysis (PEDAL). The runtime data consisting of synchronization points is used as a source of feedback for PEDAL. To demonstrate utility, we evaluate the performance of PEDAL against those of two systematic concurrency testing tools. The findings demonstrate that PEDAL can detect concurrency bugs more quickly with given inputs, and consuming less memory. To prove its scalability in a big data environment, we use PEDAL to analyze several real concurrency bugs in large scale multithread programs, namely: Apache, and MySQL.

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Acknowledgments

We thank anonymous reviews for their valuable comments, which help us improve the paper. The work in this paper is partially funded by National Science Foundation of China (NSFC 61300017, 61572097).

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Authors and Affiliations

  1. School of Software, Dalian University of Technology, Dalian, China

    Yan Hu

  2. Institute of Software, Chinese Academy of Sciences, Beijing, China

    Jun Yan

  3. University of South Australia, Adelaide, Australia

    Kim-Kwang Raymond Choo

  4. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China

    Yan Hu

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  1. Yan Hu
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  2. Jun Yan
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Correspondence to Yan Hu.

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Hu, Y., Yan, J. & Choo, KK.R. PEDAL: a dynamic analysis tool for efficient concurrency bug reproduction in big data environment. Cluster Comput 19, 153–166 (2016). https://doi.org/10.1007/s10586-016-0537-1

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  • DOI: https://doi.org/10.1007/s10586-016-0537-1

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