One Size Does Not Fit All: In-Test Workload Adaptation for Performance Testing of Enterprise Applications

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Title: One Size Does Not Fit All: In-Test Workload Adaptation for Performance Testing of Enterprise Applications
Authors: Ayala-Rivera, VanessaKaczmarski, MaciejMurphy, JohnDarisa, AmarendraPortillo Dominguez, Andres Omar
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Date: 13-Apr-2018
Online since: 2019-04-16T07:45:59Z
Abstract: Carrying out proper performance testing is considerably challenging .In particular, the identification of performance issues, as well as their root causes, is a time-consuming and complex process which typically requires several iterations of tests (as this type of issue scan depend on the input workloads), and heavily relies on human expert knowledge. To improve this process, this paper presents an automated approach (that extends some of our previous work) to dynamically adapt the workload (used by a performance testing tool) during the test runs. As a result, the performance issues of the tested application can be revealed more quickly; hence, identifying them with less effort and expertise. Our experimental evaluation has assessed the accuracy of the proposed approach and the time savings that it brings to testers. The results have demonstrated the benefits of the approach by achieving a significant decrease in the time invested in performance testing (without compromising the accuracy of the test results), while introducing a low overhead in the testing environment.
Funding Details: European Commission - European Regional Development Fund
Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2018 ACM
Keywords: PerformanceTestingWorkloadAnalysisAutomation
DOI: 10.1145/3184407.3184418
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Language: en
Status of Item: Peer reviewed
Is part of: ICPE '18 Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering
Conference Details: 9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018), Berlin, Germany, April 9-13 2018
ISBN: 978-1-4503-5095-2
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Appears in Collections:Computer Science Research Collection

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