Configuration/Infrastructure-aware testing of MapReduce programs

Morán, Jesús; Rivas, Bibiano; Alvarez, Claudio; González, Pablo J.; Caballero, Ismael; Serrano, Manuel A.

doi:10.25046/aj020111

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…Finally, the 47 papers were analyzed thoroughly to determine whether it is possible to extract the data required to answer our stated RQs. During this analysis, we found that some papers were extended versions of previously published conference/workshop papers [26][27][28][29]. Moreover, we were unable to extract data from two papers [30,31].…”

Section: Conducting the Reviewmentioning

confidence: 95%

Big Data Testing Techniques: Taxonomy, Challenges and Future Trends

Arshad¹,

Alsamhi²

2023

Computers, Materials &Amp; Continua

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Big Data is reforming many industrial domains by providing decision support through analyzing large data volumes. Big Data testing aims to ensure that Big Data systems run smoothly and error-free while maintaining the performance and quality of data. However, because of the diversity and complexity of data, testing Big Data is challenging. Though numerous research efforts deal with Big Data testing, a comprehensive review to address testing techniques and challenges of Big Data is not available as yet. Therefore, we have systematically reviewed the Big Data testing techniques' evidence occurring in the period 2010-2021. This paper discusses testing data processing by highlighting the techniques used in every processing phase. Furthermore, we discuss the challenges and future directions. Our findings show that diverse functional, non-functional and combined (functional and non-functional) testing techniques have been used to solve specific problems related to Big Data. At the same time, most of the testing challenges have been faced during the MapReduce validation phase. In addition, the combinatorial testing technique is one of the most applied techniques in combination with other techniques (i.e., random testing, mutation testing, input space partitioning and equivalence testing) to find various functional faults through Big Data testing.

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Section: Conducting the Reviewmentioning

confidence: 95%

Big Data Testing Techniques: Taxonomy, Challenges and Future Trends

Arshad¹,

Alsamhi²

2023

Computers, Materials &Amp; Continua

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“…Other authors [23], [24] identified and classified other design faults that depend upon the infrastructure configurations. In our previous work, we proposed a test approach to detect such faults in the test environment by using a simulation of the infrastructure configurations [26]. In this paper we enhance on that work with a more practical technique that improves efficiency while still detecting the majority of faults by exploiting combinatorial strategies.…”

Section: Related Workmentioning

confidence: 99%

“…In a prior work [26], we proposed an automatic technique that, given test input data, generates a thorough number of configurations (MRTest-Thorough). Then the faults are revealed when a failure occurs in one of these configurations.…”

Section: A Generation Of Infrastructure Configurations For Testingmentioning

confidence: 99%

“…In order to detect design faults, MRTest executes each test case in different configurations using one of the techniques described in the previous section (MRTest-Random and MRTest-t-Wise) or in the previous work (MRTest-Thorough) [26]. Then MRTest checks systematically that all configurations lead to equivalent outputs.…”

Section: B Execution Of Test Casesmentioning

confidence: 99%

“…In order to detect these kinds of faults in the early stages of development, our previous work simulates, in a test environment, the execution of the test cases in a thorough range of infrastructure configurations that could occur in production TR-2017-353 2 (all potential configurations) [25], [26]. This paper extends the previous approach by proposing a more efficient strategy that automatically tests the program only under the relevant and representative configurations.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Testing of Design Faults in MapReduce Applications

et al. 2018

Self Cite

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New processing models are being adopted in Big Data Engineering to overcome the limitations of traditional technology. Among them, MapReduce stands out by allowing for the processing of large volumes of data over a distributed infrastructure that can change during runtime. The developer only designs the functionality of the program and its execution is managed by a distributed system. As a consequence, a program can behave differently at each execution because it is automatically adapted to the resources available at each moment. Therefore, when the program has a design fault, this could be revealed in some executions and masked in others. However, during testing, these faults are usually masked because the test infrastructure is stable, and they are only revealed in production because the environment is more aggressive with infrastructure failures, among other reasons. This paper proposes new testing techniques aimed to detect these design faults by simulating different infrastructure configurations. The testing techniques generate a representative set of infrastructure configurations that as whole are more likely to reveal failures using Random testing, and Partition testing together with Combinatorial testing. The techniques are automated by using a test execution engine called MRTest that is able to detect these faults using only the test input data, regardless of the expected output. Our empirical evaluation shows that MRTest can automatically detect these design faults within a reasonable time.

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