一句话：针对超大规模 CI/CD 中全量测试成本过高的问题，提出数据驱动的测试选择模型，为每次变更只运行相关测试子集。
大规模服务依赖 CI/CD 流程维持敏捷性与代码质量。基于变更的测试对发现缺陷至关重要，但在每小时有成千上万次提交的规模下，每次变更后都运行全量测试成本极高。测试选择模型通过为每次变更只执行一个测试子集来应对该问题。本文标题为“大规模数据驱动的测试选择”，其核心是采用数据驱动方法进行测试选择。摘要仅概述了问题与一般思路，未提供具体方法细节、实验设置或评估结果，因此无法总结其关键技术贡献与量化效果。
Large-scale services depend on Continuous Integration/Continuous Deployment (CI/CD) processes to maintain their agility and code-quality. Change-based testing plays an important role in finding bugs, but testing after every change is prohibitively expensive at a scale where thousands of changes are committed every hour. Test selection models deal with this issue by running a subset of tests for every change.
DOI 原文 ·
@inproceedings{paperbot6322,
title = {Data-driven test selection at scale},
author = {Sonu Mehta and Farima Farmahinifarahani and Ranjita Bhagwan and Suraj Guptha and Sina Jafari and Rahul Kumar and Vaibhav Saini and Anirudh Santhiar},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3473916}
}