Name
Title | ||
|---|---|---|
One Size Does Not Fit All: In-Test Workload Adaptation for Performance Testing of Enterprise Applications. |
Abstract | ||
|---|---|---|
The identification of workload-dependent 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 issues can depend on the input workloads), and heavily relies on human expert knowledge. To improve this process, this paper presents an automated approach 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.
|
Year | Venue | Field |
|---|---|---|
2018 | ICPE | Workload,Control engineering,Automation,Engineering,Reliability engineering |
DocType | ISBN | Citations |
Conference | 978-1-4503-5095-2 | 0 |
PageRank | References | Authors |
0.34 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vanessa Ayala-Rivera | 1 | 18 | 3.96 |
| Maciej Kaczmarski | 2 | 1 | 0.69 |
| John Murphy | 3 | 75 | 10.07 |
| Amarendra Darisa | 4 | 0 | 0.34 |
| A. Omar Portillo-Dominguez | 5 | 25 | 5.68 |