TaxDC

Leesatapornwongsa, Tanakorn; Lukman, Jeffrey F.; Lu, Shan; Gunawi, Haryadi S.

doi:10.1145/2954679.2872374

Cited by 15 publications

(3 citation statements)

References 39 publications

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“…Concurrency error detect. Another group of works is on detecting concurrency errors including Razzer [9] and TaxDC [11] [1]. Existing methods mostly are based on monitoring memory access and find concurrency errors, or through static analyze of the code fragments.…”

Section: Related Workmentioning

confidence: 99%

Hunting Garbage Collection Related Concurrency Bugs through Critical Condition Restoration

Zhou

Lan

Venkataramani

2020

Proceedings of the 2020 ACM Workshop on Forming an Ecosystem Around Software Transformation

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With the increasing popularity of multi-core processors and multithread languages/frameworks, race conditions-which are nondeterministic by nature-are becoming a main root cause for concurrency bugs. It opens doors to malicious attacks such as remote code execution and denial of service attacks, potentially putting millions of users in danger. Yet, such non-deterministic racing conditions are often difficult to identify or reproduce in standard program testing. In this paper, we focus on the Garbage-Collection (GC) feature, which is known to be a frequent victim of concurrency bugs in many software systems. We develop a new approach to facilitate the testing of GC-related bugs through critical condition restoration. In particular, we propose a risk-score mechanism to quantify the risk of GC-related bugs in target functions and leverage the score to select appropriate testing parameters and garbage generation strategy, with a higher chance of producing the critical condition. Our experimental results show that the proposed approach could significantly improve the probability of finding GC-related bugs (from 0 in condition-oblivious testing to 14.8 bugs identified in our experiment) while incurring only around 26% execution overhead. CCS CONCEPTS • Software and its engineering → Software testing and debugging; Garbage collection; Software reliability.

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Section: Related Workmentioning

confidence: 99%

Hunting Garbage Collection Related Concurrency Bugs through Critical Condition Restoration

Zhou

Lan

Venkataramani

2020

Proceedings of the 2020 ACM Workshop on Forming an Ecosystem Around Software Transformation

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“…Semantic‐aware model checking, SAMC 49, verifies distributed systems by supplying a system‐specific test and monitoring harness on top of a framework to verify networked systems. In that framework, all possible schedules (interleavings) of networked messages are tested, based on a given system test that executes a particular sequence of API calls for each client.…”

Section: Related Workmentioning

confidence: 99%

“…In that framework, all possible schedules (interleavings) of networked messages are tested, based on a given system test that executes a particular sequence of API calls for each client. Existing work has applied this approach to a model of ZooKeeper 49 that is written in Java but about an order of magnitude smaller than the real implementation. In contrast to this, our work tests many different sequences of API calls, but executes one thread schedule each time, on the actual implementation of ZooKeeper rather than a model of it.…”

Section: Related Workmentioning

confidence: 99%

Model‐based testing of Apache ZooKeeper: Fundamental API usage and watchers

Artho

Banzai

Gros

et al. 2019

Software Testing Verif & Rel

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In this paper, we extend work on model-based testing for Apache ZooKeeper, to handle watchers (triggers) and improve scalability. In a distributed asynchronous shared storage like ZooKeeper, watchers deliver notifications on state changes. They are difficult to test because watcher notifications involve an initial action that sets the watcher, followed by another action that changes the previously seen state. We show how to generate test cases for concurrent client sessions executing against ZooKeeper with the tool Modbat. The tests are verified against an oracle that takes into account all possible timings of network communication. The oracle has to verify that there exists a chain of events that triggers both the initial callback and the subsequent watcher notification. We show in detail how the oracle computes whether watch triggers are correct and how the model was adapted and improved to handle these features. Together with a new search improvement that increases both speed and accuracy, we are able to verify large test setups and confirm several defects with our model.

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ChIP: A Choreographic Integration Process

Giallorenzo

Lanese

Russo

2018

Lecture Notes in Computer Science

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Over the years, organizations acquired disparate software systems, each answering one specific need. Currently, the desirable outcomes of integrating these systems (higher degrees of automation and better system consistency) are often outbalanced by the complexity of mitigating their discrepancies. These problems are magnified in the decentralized setting (e.g., cross-organizational cases) where the integration is usually dealt with ad-hoc "glue" connectors, each integrating two or more systems. Since the overall logic of the integration is spread among many glue connectors, these solutions are difficult to program correctly (making them prone to misbehaviors and system blocks), maintain, and evolve. In response to these problems, we propose ChIP, an integration process advocating choreographic programs as intermediate artifacts to refine high-level global specifications (e.g., UML Sequence Diagrams), defined by the domain experts of each partner, into concrete, distributed implementations. In ChIP, once the stakeholders agree upon a choreographic integration design, they can automatically generate the respective local connectors, which are guaranteed to faithfully implement the described distributed logic. In the paper, we illustrate ChIP with a pilot from the EU EIT Digital project SMAll, aimed at integrating pre-existing systems from government, university, and transport industry.

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TaxDC

Cited by 15 publications

References 39 publications

Hunting Garbage Collection Related Concurrency Bugs through Critical Condition Restoration

Hunting Garbage Collection Related Concurrency Bugs through Critical Condition Restoration

Model‐based testing of Apache ZooKeeper: Fundamental API usage and watchers

ChIP: A Choreographic Integration Process

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