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Event Race Detection for Node.js Using Delay Injections

Authors: Endo, Andre Takeshi; Møller, Anders;

Event Race Detection for Node.js Using Delay Injections

Abstract

Node.js is a widely used platform for building JavaScript server-side web applications, desktop applications, and software engineering tools. Its asynchronous execution model is essential for performance, but also gives rise to event races, which cause many subtle bugs that can be hard to detect and reproduce. Current solutions to expose such races are based on modifications of the source code of the Node.js system or on guided executions using complex happens-before modeling. This paper presents a simpler and more effective approach called NACD that works by dynamically instrumenting core asynchronous operations in the Node.js runtime system to inject delays and thereby reveal event race bugs. It consists of a small, robust runtime instrumentation module implemented in JavaScript that is configured by a flexible JSON model of the essential parts of the Node.js API. Experimental results show that NACD can reproduce event race bugs with higher probability and fewer runs than state-of-the-art tools.

Keywords

JavaScript, race conditions, callback interleaving, flaky tests, event races, ddc: ddc:004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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