一句话：提出工业级测试选择工具MOTSD，以测试套件可诊断性和历史指标为目标，用粒子群优化缩短回归测试反馈。
大型软件系统每次变更后执行完整回归测试耗时过长，难以在CI流程中及时反馈。本文介绍在OutSystems工业场景下开发的工具MOTSD，在C#代码库中实现多目标测试选择，以测试套件可诊断性指标和历史指标作为优化目标，并使用粒子群优化算法搜索测试子集。论文报告了实现过程中遇到的挑战、当前实验结果以及该方法在工业环境中应用时的局限性。目标是为开发者提供更短、更早的反馈回路，替代等待CI流水线的缓慢反馈。
Performing full regression testing every time a change is made on large software systems tends to be unfeasible as it takes too long to run all the test cases. The main motivation of this work was to provide a shorter and earlier feedback loop to the developers at OutSystems when a change is made (instead of having to wait for slower feedback from a CI pipeline). The developed tool, MOTSD, implements a multi-objective test selection approach in a C# code base using a test suite diagnosability metric and historical metrics as objectives and it is powered by a particle swarm optimization algorithm. This paper presents implementation challenges, current experimental results and limitations of the developed approach when applied in an industrial context.
DOI 原文 ·
@inproceedings{paperbot5048,
title = {An industrial application of test selection using test suite diagnosability},
author = {Daniel Correia},
booktitle = {FSE},
year = {2019},
doi = {10.1145/3338906.3342493}
}