Who goes first? detecting go concurrency bugs via message reordering

Liu, Ziheng; Xia, Shihao; Liang, Yu Chih; Song, Linhai; Hong, Hui

doi:10.1145/3503222.3507753

Cited by 9 publications

(2 citation statements)

References 41 publications

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“…However, those proposals do not look for data consistency violations caused by concurrency bugs. Instead of using systematic testing, GFuzz proposes employing a fuzzing technique that has been developed instead of using the deterministic ordering of concurrent events [9]. Also, researchers explored a random testing technique by randomly sampling partial order to more accurately pinpoint the sequence of event execution that is more likely to cause errors [52].…”

Section: Distributed Systems Testingmentioning

confidence: 99%

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VConMC: Enabling Consistency Verification for Distributed Systems Using Implementation-Level Model Checkers and Consistency Oracles

Kim

2024

Electronics

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Many cloud services are relying on distributed key-value stores such as ZooKeeper, Cassandra, HBase, etc. However, distributed key-value stores are notoriously difficult to design and implement without any mistakes. Because data consistency is the contract for clients that defines what the correct values to read are for a given history of operations under a specific consistency model, consistency violations can confuse client applications by showing invalid values. As a result, serious consequences such as data loss, data corruption, and unexpected behavior of client applications can occur. Software bugs are one of main reasons why consistency violations may occur. Formal verification techniques may be used to make designs correct and minimize the risks of having bugs in the implementation. However, formal verification is not a panacea due to limitations such as the cost of verification, inability to verify existing implementations, and human errors involved. Implementation-level model checking has been heavily explored by researchers for the past decades to formally verify whether the underlying implementation of distributed systems have bugs or not. Nevertheless, previous proposals are limited because their invariant checking is not versatile enough to check for the wide spectrum of consistency models, from eventual consistency to strong consistency. In this work, consistency oracles are employed for consistency invariant checking that can be used by implementation-level model checkers to formally verify data consistency model implementations of distributed key-value stores. To integrate consistency oracles with implementation-level distributed system model checkers, the partial-order information obtained via API is leveraged to avoid the exhaustive search during consistency invariant checking. Our evaluation results show that, by using the proposed method for consistency invariant checking, our prototype model checker, VConMC, can detect consistency violations caused by several real-world software bugs in a well-known distributed key-value store, ZooKeeper.

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Section: Distributed Systems Testingmentioning

confidence: 99%

“…However, previous works have not studied how to detect violations of consistency models well when they actually occur. One line of previous works either leverages random testing approaches like fuzzing methods [8][9][10][11][12][13]. However, fuzzing cannot systematically explore the state space of SUTs; therefore, it may miss some bugs.…”

Section: Introductionmentioning

confidence: 99%