论文标题
测试或不进行测试：一种主动确定完整绩效测试启动的方法
To test, or not to test: A proactive approach for deciding complete performance test initiation
论文作者
论文摘要
软件性能测试需要一组输入，以行使代码的不同部分以识别性能问题。但是，在大量输入上运行测试可能是一个非常耗时的过程。当测试输入不断增长时，这更有问题，这是一个大规模的科学组织，例如CERN，进行科学实验的过程会产生大量数据，这些数据由物理学家分析，从而导致新的科学发现。因此，在本文中，我们提出了一种基于聚类技术的测试输入最小化方法，以处理对增长数据的测试问题。此外，我们使用聚类信息提出了一种方法，建议测试人员决定何时运行完整的测试套件进行性能测试。为了证明我们的方法的疗效，我们将其应用于CERN使用的Web服务的两个不同代码更新，我们发现我们的方法对使用瓶颈进行更新的性能测试启动的建议是有效的。
Software performance testing requires a set of inputs that exercise different sections of the code to identify performance issues. However, running tests on a large set of inputs can be a very time-consuming process. It is even more problematic when test inputs are constantly growing, which is the case with a large-scale scientific organization such as CERN where the process of performing scientific experiment generates plethora of data that is analyzed by physicists leading to new scientific discoveries. Therefore, in this article, we present a test input minimization approach based on a clustering technique to handle the issue of testing on growing data. Furthermore, we use clustering information to propose an approach that recommends the tester to decide when to run the complete test suite for performance testing. To demonstrate the efficacy of our approach, we applied it to two different code updates of a web service which is used at CERN and we found that the recommendation for performance test initiation made by our approach for an update with bottleneck is valid.
