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

测试优先级和选择在测试过程优化中非常重要，以有效地利用测试资源，特别是在DevOps实践的持续集成中对不同代码提交进行回归测试时。适当的知识处理和学习技术可以用来处理大量和通用的信息来源，以有效地处理每个代码提交的特性以及软件基线，并评估它们的质量特征。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，以与每次代码更改引入的更改和风险保持一致。本研究的关键贡献将包括:1)发展战略,可以自动选择测试套件,可以影响改变并确定最可能的失败基于给定的变化,(2)建模的大小、普遍性和范围的变化,并验证通过确定一组最优的测试套件执行,和(3)使用机器学习模型开发人员技能改变一个特定区域,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.