NodeRacer: Event Race Detection for Node.js Applications

Endo, André Takeshi; Möller, Anders

doi:10.1109/icst46399.2020.00022

Cited by 20 publications

(4 citation statements)

References 30 publications

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“…In addition, current detection tools focus mainly on languages like Java, Python and Ruby. Searching through the different resources and the recent literature surveys on flaky tests [36], [37], we found only one tool, NodeRacer [38], that aims to manifest test flakiness causes by event races in JavaScript programs. There is related work on detecting other concurrency issues in JavaScript programs such as NRace [39] or atomicity violations, NodeAV [40].…”

Section: Discussionmentioning

confidence: 99%

An Empirical Study of Flaky Tests in JavaScript

Hashemi

Tahir

Rasheed

2022

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Flaky tests (tests with non-deterministic outcomes) can be problematic for testing efficiency and software reliability. Flaky tests in test suites can also significantly delay software releases. There have been several studies that attempt to quantify the impact of test flakiness in different programming languages (e.g., Java and Python) and application domains (e.g., mobile and GUI-based). In this paper, we conduct an empirical study of the state of flaky tests in JavaScript. We investigate two aspects of flaky tests in JavaScript projects: the main causes of flaky tests in these projects and common fixing strategies. By analysing 452 commits from large, top-scoring JavaScript projects from GitHub, we found that flakiness caused by concurrency-related issues (e.g., async wait, race conditions or deadlocks) is the most dominant reason for test flakiness. The other top causes of flaky tests are operating system-specific (e.g., features that work on specific OS or OS versions) and network stability (e.g., internet availability or bad socket management). In terms of how flaky tests are dealt with, the majority of those flaky tests (>80%) are fixed to eliminate flaky behaviour and developers sometimes skip, quarantine or remove flaky tests.

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Section: Discussionmentioning

confidence: 99%

An Empirical Study of Flaky Tests in JavaScript

Hashemi

Tahir

Rasheed

2022

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“…Shaker [37] exposes flakiness by adding noise to the environment in the form of tasks that also stress the CPU and memory whilst the test suite is executed. NodeRacer [38] is a tool for JavaScript programs running on node.js with which accelerates manifestation of event races that can cause test flakiness. It uses instrumentation and builds a model consisting of a happens-after relation for callbacks.…”

Section: A Flaky Test Detection Techniquesmentioning

confidence: 99%

Flaky Test Sanitisation via On-the-Fly Assumption Inference for Tests with Network Dependencies

Dietrich¹,

Rasheed²,

Tahir³

2022

Preprint

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Flaky tests cause significant problems as they can interrupt automated build processes that rely on all tests succeeding and undermine the trustworthiness of tests. Numerous causes of test flakiness have been identified, and program analyses exist to detect such tests. Typically, these methods produce advice to developers on how to refactor tests in order to make test outcomes deterministic.We argue that one source of flakiness is the lack of assumptions that precisely describe under which circumstances a test is meaningful. We devise a sanitisation technique that can isolate flaky tests quickly by inferring such assumptions on-the-fly, allowing automated builds to proceed as flaky tests are ignored. We demonstrate this approach for Java and Groovy programs by implementing it as extensions for three popular testing frameworks (JUnit4, JUnit5 and Spock) that can transparently inject the inferred assumptions. If JUnit5 is used, those extensions can be deployed without refactoring project source code.We demonstrate and evaluate the utility of our approach using a set of six popular real-world programs, addressing known test flakiness issues in these programs caused by dependencies of tests on network availability. We find that our method effectively sanitises failures induced by network connectivity problems with high precision and recall.Index Terms-flaky tests, non-determinism, regression testing3 This refers to the number of .class files found containing methods annotated as tests, methods with @Test or @ParameterizedTest annotations 4 Standard here refers to the assumptions and assertions defined as static methods in org.junit.jupiter.api.Assumptions (JU-nit5), org.junit.Assume (JUnit4), org.junit.jupiter.api.-Assertions (JUnit5) and org.junit.Assert (JUnit4) respectively, we are counting the call sites for those methods in the respective classes.

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“…This work does not focus on the arbitrary command execution or arbitrary code execution vulnerabilities in the low layer of Node.js (i.e., binding module layer and dependency module layer). Race conditions [39], DOS attacks (such as regular expression DoS) [40,41], hidden property abuse [42] in Node.js applications, and global variable alterations are also out of the scope of this work, as these attacks are usually triggered without invoking system calls. Installtime attacks [43] is out of the scope of this work since the attacks are triggered during the installation.…”

Section: Threat Modelmentioning

confidence: 99%

“…Installtime attacks [43] is out of the scope of this work since the attacks are triggered during the installation. Moreover, these can be addressed by complementary techniques [20,[39][40][41][42][43][44].…”

Section: Threat Modelmentioning

confidence: 99%

Learning ability of top university students in China: Shanghai Jiao Tong University as a case study

Wang

2022

SN Soc Sci

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The network illustrates the interrelationships among diverse entities and has attracted considerable attention. A wide range of applications makes the network a popular modeling tool, including but not limited to social networks [1], business administration [2], and city construction [3]. In these fields, quantifying network value is essential. By examining network value, companies gain insights into consumer behavior, allowing them to refine marketing strategies accordingly [4]. Similarly, investigating network value can assist urban planners in better understanding the utilization and demands for public facilities, ultimately optimizing city construction [5]. To better understand network value, researchers have established some influential laws that describe network value in terms of the number of neighbors, edges, and subgraphs.Sarnoff 's Law. In the 1940s, David Sarnoff proposed that the value of a broadcasting network is directly proportional to the number of nodes (audience), which is also known as Sarnoff's law [6]. For example, in the case of TV programs, the network value increases linearly with the number of the audience, because the growing number of audiences allows advertisers to access more potential customers. This results in more advertisers, increased revenues, and higher broadcast media demand. Formally, for a network with n nodes, the communication value is Θ(n). This law was initially applied to the film industry, later extended to television, and usually represents one-way communication. Broadcasting can only transmit messages unidirectionally to users, but cannot spread information within users.Metcalfe's Law. With the conferral of the Turing Award on Robert Metcalfe in 2022, Metcalfe's law[7] has regained attention. Metcalfe's law was proposed on the background of the increasing number of Ethernet users and growing attention to the interconnection value of networks. Metcalfe argues that if the value of each node (terminal) is equal, the value of a network is proportional to the number of edges. One of the classic illustrations of Metcalfe's law lies in communication networks, where a network with n users can provide interconnection value proportional to approximately n 2 , i.e. Θ(n 2 ), as each user can communicate with the other n − 1 users in the network. Despite the many challenges associated with the development of the network, such as network scale, connection quality, and network design, Metcalfe's law remains applicable in some scenarios, especially in the field of cloud computing [8]. The increasing number of cloud computing users leads to the availability of more resources, which in turn attracts additional users, creating a virtuous cycle of growth. As a theory for describing the interconnection value of networks, Metcalfe's law is held in high regard and possesses significant implications for communication networks, the Internet, and social networks.Reed's Law. In 2000, David Reed proposed Reed's law [9] for group-forming networks (GFNs). He argued that network value depends not ...

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NodeRacer: Event Race Detection for Node.js Applications

Cited by 20 publications

References 30 publications

An Empirical Study of Flaky Tests in JavaScript

An Empirical Study of Flaky Tests in JavaScript

Flaky Test Sanitisation via On-the-Fly Assumption Inference for Tests with Network Dependencies

Learning ability of top university students in China: Shanghai Jiao Tong University as a case study

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