一句话：通过分析性能缺陷报告，自动提取执行命令与输入参数，生成指导性能测试用例生成的测试框架。
性能缺陷会导致系统变慢和用户体验下降。现有自动化性能测试多通过选择工作负载值来增大执行时间，通常假设初始测试用例已含正确的输入参数组合，但可配置性强的真实应用参数组合巨大，该假设未必成立。本文人工分析了 Apache HTTP Server、MySQL 和 Mozilla Firefox 三个大型开源项目的 300 份缺陷报告，发现暴露性能缺陷常需多个输入参数组合，且某些参数频繁参与。基于此，作者提出并评估 PerfLearner：从性能缺陷报告描述中抽取执行命令和输入参数，生成测试框架以指导实际性能测试用例生成。
Software performance is important for ensuring the quality of software products. Performance bugs, defined as programming errors that cause significant performance degradation, can lead to slow systems and poor user experience. While there has been some research on automated performance testing such as test case generation, the main idea is to select workload values to increase the program execution times. These techniques often assume the initial test cases have the right combination of input parameters and focus on evolving values of certain input parameters. However, such an assumption may not hold for highly configurable real-word applications, in which the combinations of input parameters can be very large. In this paper, we manually analyze 300 bug reports from three large open source projects - Apache HTTP Server, MySQL, and Mozilla Firefox. We found that 1) exposing performance bugs often requires combinations of multiple input parameters, and 2) certain input parameters are frequently involved in exposing performance bugs. Guided by these findings, we designed and evaluated an automated approach, PerfLearner, to extract execution commands and input parameters from descriptions of performance bug reports and use them to generate test frames for guiding actual performance test case generation.
DOI 原文 ·
@inproceedings{paperbot4726,
title = {PerfLearner: learning from bug reports to understand and generate performance test frames},
author = {Xue Han and Tingting Yu and David Lo},
booktitle = {ASE},
year = {2018},
doi = {10.1145/3238147.3238204}
}