PReT:一个基于阶段的自动回归测试工具

Arnamoy Bhattacharyya, C. Amza
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引用次数: 5

摘要

在本文中,我们提出了我们的工具PReT,它可以在软件上执行自动性能回归测试。PReT基于应用程序快照进行非侵入性分析,以学习性能回归测试的行为,并且可以通过将当前行为与学习的模型进行比较来识别测试行为中的任何更改。PReT使用应用程序堆栈跟踪注释资源使用概况,并使用k-means的变体来在线学习每个回归测试的模型。最重要的是,PReT使用软件的版本信息来识别引入性能问题的变更(如果有的话)。我们将展示PReT在正确识别Cassandra数据库服务器中的两个实际性能错误方面的有用性。我们表明,与纯粹基于资源利用的表征技术相比,PReT能够以更高的准确性表征正在为软件运行的性能测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PReT: A Tool for Automatic Phase-Based Regression Testing
In this paper, we present our tool PReT, which performs automatic performance regression testing on software. PReT does non-intrusive profiling based on application snapshots to learn behaviour for performance regression tests and can identify any changes in the testing behaviour by comparing the current behaviour against a learned model. PReT annotates resource usage profiles with application stacktraces and uses a variation of k-means to learn the models per regression test online. On top of that, PReT uses version information of the software to identify change(s) that introduce(s) performance issue(s) if any. We show the usefulness of PReT in correctly identifying two real world performance bugs in Cassandra database server. We show that PReT is able to characterize the performance tests being run for the software with higher accuracy than a purely resource utilization based characterization technique.
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