Towards Ex Vivo Testing of MapReduce Applications

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

doi:10.1109/qrs.2017.17

Cited by 7 publications

(7 citation statements)

References 49 publications

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“…Moran et al [38] have proposed an Ex Vivo testing framework called MapReduce Ex vivo testing (MrExist) that takes advantage of the production information to detect faults. The authors identified some faults in their previous work [39].…”

Section: Ex Vivo Testingmentioning

confidence: 99%

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: Ex Vivo Testingmentioning

confidence: 99%

Big Data Testing Techniques: Taxonomy, Challenges and Future Trends

Arshad¹,

Alsamhi²

2023

Computers, Materials &Amp; Continua

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“…The concept of ex vivo testing has been already exploited in several contexts. For instance, it has been used to test MapReduce applications based on the data computed in the field, 6 to test autonomous vehicles based on actual usages scenarios, 8 and in self‐adaptive systems 9 . Furthermore field information can be exploited not only to synthesize test cases but also to influence their order of execution 56 or increase the number of tests can be executed across versions 57 .…”

Section: Related Workmentioning

confidence: 99%

“…To timely consider the actual behavior of the services in operation also during in‐house testing stages, it is possible to exploit ex vivo testing strategies 5 . Ex vivo testing uses information extracted from the field to augment existing test cases or produce new test cases that can be used to validate software upgrades consistently with field usages 6,7 . Indeed, ex vivo testing may limit the number of faults that escape in‐house testing due to the lack of knowledge of the behavior of the software in operation, reducing the number of failures that are discovered late in the field, once services have been updated.…”

Section: Introductionmentioning

confidence: 99%

“…5 Ex vivo testing uses information extracted from the field to augment existing test cases or produce new test cases that can be used to validate software upgrades consistently with field usages. 6,7 Indeed, ex vivo testing may limit the number of faults that escape in-house testing due to the lack of knowledge of the behavior of the software in operation, reducing the number of failures that are discovered late in the field, once services have been updated. Relevant applications of ex vivo testing include MapReduce applications, 6 autonomous vehicles, 8 and self-adaptive systems.…”

mentioning

confidence: 99%

“…6,7 Indeed, ex vivo testing may limit the number of faults that escape in-house testing due to the lack of knowledge of the behavior of the software in operation, reducing the number of failures that are discovered late in the field, once services have been updated. Relevant applications of ex vivo testing include MapReduce applications, 6 autonomous vehicles, 8 and self-adaptive systems. 9 This paper studies ex vivo testing applied to microservice architectures.…”

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confidence: 99%

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ExVivoMicroTest: ExVivo Testing of Microservices

Gazzola

Goldstein²,

Mariani

et al. 2022

J Software Evolu Process

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Microservice‐based applications consist of multiple services that can evolve independently. When a service must be updated, it is first tested with in‐house regression test suites. However, the test suites that are executed are usually designed without the exact knowledge about how the services will be accessed and used in the field; therefore, they may easily miss relevant test scenarios, failing to prevent the deployment of faulty services. To address this problem, we introduce ExVivoMicroTest, an approach that analyzes the execution of deployed services at run‐time in the field, in order to generate test cases for future versions of the same services. ExVivoMicroTest implements lightweight monitoring and tracing capabilities, to inexpensively record executions that can be later turned into regression test cases that capture how services are used in the field. To prevent accumulating an excessive number of test cases, ExVivoMicroTest uses a test coverage model that can discriminate the recorded executions between the ones that are worth to be turned into test cases and the ones that should be discarded. The resulting test cases use a mocked environment that fully isolates the service under test from the rest of the system to faithfully reply interactions. We assessed ExVivoMicroTest with the PiggyMetrics and Train Ticket open source microservice applications and studied how different configurations of the monitoring and tracing logic impact on the capability to generate test cases.

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In vivo test and rollback of Java applications as they are

Bertolino

Angelis

Miranda

et al. 2023

Software Testing Verif & Rel

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SummaryModern software systems accommodate complex configurations and execution conditions that depend on the environment where the software is run. While in house testing can exercise only a fraction of such execution contexts, in vivo testing can take advantage of the execution state observed in the field to conduct further testing activities. In this paper, we present the Groucho approach to in vivo testing. Groucho can suspend the execution, run some in vivo tests, rollback the side effects introduced by such tests, and eventually resume normal execution. The approach can be transparently applied to the original application, even if only available as compiled code, and it is fully automated. Our empirical studies of the performance overhead introduced by Groucho under various configurations showed that this may be kept to a negligible level by activating in vivo testing with low probability. Our empirical studies about the effectiveness of the approach confirm previous findings on the existence of faults that are unlikely exposed in house and become easy to expose in the field. Moreover, we include the first study to quantify the coverage increase gained when in vivo testing is added to complement in house testing.

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Towards Ex Vivo Testing of MapReduce Applications

Cited by 7 publications

References 49 publications

Big Data Testing Techniques: Taxonomy, Challenges and Future Trends

Big Data Testing Techniques: Taxonomy, Challenges and Future Trends

ExVivoMicroTest: ExVivo Testing of Microservices

In vivo test and rollback of Java applications as they are

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