Predictive Test Selection
Predictive Test Selection
复制标题
预测测试选择
DOI：
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复制
发表时间：
2018
期刊：
影响因子：
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通讯作者：
S. Chandra
中科院分区：
文献类型：
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作者：
Mateusz Machalica;A. Samylkin;Meredith Porth;S. Chandra
基于变化的测试是Facebook持续集成的关键组成部分。然而，大量的测试加上提交到我们的单一存储库的高变更率，使得在每个变更上运行所有可能受影响的测试是不可行的。我们提出了一个新的预测测试选择策略，选择一个子集的测试，以行使每一个变化提交到持续集成系统。该策略是使用基本的机器学习技术从历史测试结果的大型数据集中学习的。部署到生产环境中，该策略将测试代码更改的总基础设施成本降低了两倍，同时保证超过95%的单独测试失败和超过99.9%的错误更改仍然会报告给开发人员。我们在这里提出的方法也解释了测试结果的非确定性，也称为测试片状性。
Change-based testing is a key component of continuous integration at Facebook. However, a large number of tests coupled with a high rate of changes committed to our monolithic repository make it infeasible to run all potentially-impacted tests on each change. We propose a new predictive test selection strategy which selects a subset of tests to exercise for each change submitted to the continuous integration system. The strategy is learned from a large dataset of historical test outcomes using basic machine learning techniques. Deployed in production, the strategy reduces the total infrastructure cost of testing code changes by a factor of two, while guaranteeing that over 95% of individual test failures and over 99.9% of faulty changes are still reported back to developers. The method we present here also accounts for the non-determinism of test outcomes, also known as test flakiness.
跨 JVM 边界的回归测试选择
DOI：
10.1145/3106237.3106297
发表时间：
2017
期刊：
Joint Meeting on Foundations of Software Engineering
影响因子：
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作者：
Celik, Ahmet;Vasic, Marko;Milicevic, Aleksandar;Gligoric, Milos
通讯作者：
Gligoric, Milos