Test case prioritization using machine learning for large-scale continuous integration environment

测试优先级和测试选择在测试流程优化中非常重要，以有效利用测试资源，特别是在不断整合DevOps实践中针对不同代码提交的回归测试。可以使用适当的知识处理和学习技术来处理众多和多种多样的信息源，以有效地处理每一代码提交的特点以及软件基线，并评价其质量特征。IBM的认知测试系统有一个长期目标，即研究如何使用机器学习和人工智能技术来简化和智能地调整测试环境，以与每次代码更改带来的更改和风险保持一致。这项研究中的一些关键贡献包括：1)开发一种策略，该策略可以自动选择可能受到更改影响的测试套件，并根据给定的更改识别最有可能失败的测试套件；(2)对更改的大小、普及性和范围进行建模，并通过确定要执行的最优测试套件集来验证这一点；以及(3)使用机器学习来模拟开发人员更改给定领域的技能并相应地调整测试策略。

Test prioritization and selection is of great importance in test process optimization to make efficient use of testing resources, especially when it comes to regression testing for different code submissions in the continuous integration of DevOps practice. Appropriate knowledge processing and learning techniques to deal with numerous and versatile sources of information can be used to address efficiently the peculiarities of each code submissions as well as software baselines and evaluate their quality characteristics. The cognitive test system in IBM has a long term goal to look at how to use machine learning and AI techniques to streamline and intelligently adapt testing environment to align with the changes and risks being introduced with each code change. A number of key contributions in this research will include: 1) developing a strategy that can automatically select the test suites that could be impacted by a change and identify the most likely ones to fail based on a given change, (2) modeling the size, pervasiveness and scope of a change and verify that by determining an optimal set of test suites to execute, and (3) using machine learning to model a developers skill in changing a given area and adjust test strategy accordingly.