Automatic Testing of Design Faults in MapReduce Applications

Morán, Jesús; Bertolino, Antonia; Riva, Claudio de la; Tuya, Javier

doi:10.1109/tr.2018.2802047

Cited by 16 publications

(21 citation statements)

References 57 publications

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“…There are several proposals for testing different parts of cloud systems, like symbolic execution [16], fault injection in the target system [17] and random testing [18], [19]. These approaches can be categorised into two major groups: testing the cloud and testing in the cloud (also known as cloud-based testing or cloud testing).…”

Section: Testing Of Cloud Systemsmentioning

confidence: 99%

TEA-Cloud: A Formal Framework for Testing Cloud Computing Systems

et al. 2021

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The validation of a cloud system can be complicated by the size of the system, the number of users that can concurrently request services, and the virtualisation used to give the illusion of using dedicated machines. Unfortunately, it is not feasible to use conventional testing methods with cloud systems. This paper proposes a framework, called TEA-Cloud, that integrates simulation with testing methods for validating cloud system designs. Testing is applied on both functional and non-functional aspects of the cloud, like performance and cost. The aim of the framework is to provide a complete methodology to help users to model both software and hardware parts of cloud systems and automatically test the validity of these clouds using a cost-effective approach. Metamorphic testing is used to overcome the lack of an oracle that checks whether the behaviour observed in testing is allowed. Metamorphic testing is based on metamorphic relations (MRs). We define three families of MRs, which target issues such as performance, resource provisioning and cost. TEA-Cloud was evaluated through an empirical study that used fault seeding (mutation) and ten MRs for testing different cloud configurations. The results were promising, with TEA-Cloud finding all seeded faults.

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Section: Testing Of Cloud Systemsmentioning

confidence: 99%

TEA-Cloud: A Formal Framework for Testing Cloud Computing Systems

et al. 2021

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“…Morán et al [56] presented a testing approach that simulates different infrastructure configurations to detect software faults easily masked in a stable test environment but revealed in production. Their solution is based on combinatorial testing, partition testing, and random testing to choose the infrastructure configurations.…”

Section: Related Workmentioning

confidence: 99%

SPOT: Testing Stream Processing Programs with Symbolic Execution and Stream Synthesizing

Qian

2021

Applied Sciences

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Adoption of distributed stream processing (DSP) systems such as Apache Flink in real-time big data processing is increasing. However, DSP programs are prone to be buggy, especially when one programmer neglects some DSP features (e.g., source data reordering), which motivates development of approaches for testing and verification. In this paper, we focus on the test data generation problem for DSP programs. Currently, there is a lack of an approach that generates test data for DSP programs with both high path coverage and covering different stream reordering situations. We present a novel solution, SPOT (i.e., Stream Processing Program Test), to achieve these two goals simultaneously. At first, SPOT generates a set of individual test data representing each path of one DSP program through symbolic execution. Then, SPOT composes these independent data into various time series data (a.k.a, stream) in diverse reordering. Finally, we can perform a test by feeding the DSP program with these streams continuously. To automatically support symbolic analysis, we also developed JPF-Flink, a JPF (i.e., Java Pathfinder) extension to coordinate the execution of Flink programs. We present four case studies to illustrate that: (1) SPOT can support symbolic analysis for the commonly used DSP operators; (2) test data generated by SPOT can more efficiently achieve high JDU (i.e., Joint Dataflow and UDF) path coverage than two recent DSP testing approaches; (3) test data generated by SPOT can more easily trigger software failure when comparing with those two DSP testing approaches; and (4) the data randomly generated by those two test techniques are highly skewed in terms of stream reordering, which is measured by the entropy metric. In comparison, it is even for test data from SPOT.

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“…But the success of all these applications and apps depends on the quality of application development. Mobile apps have done a very vital role in all 􀅫ields of life [43].…”

Section: N Device and Os Fragmentation Of Mobile Apps 1) Conceptualization And Planning For Mobile Apps Quality Assurancementioning

confidence: 99%