AI Methods for Automated Software Testing

自动化软件测试的人工智能方法

• 批准号: 544119-2019
• 负责人: Bolic, Miodrag
• 金额: $ 1.82万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=680243
• 关键词: AI; Methods; Automated; Software; Testing

In large companies, software codebase is very large and it is changing really fast. The teams spend a lot of time running tests even for very small changes in the code. To find the exact change that caused tests to fall, developers run every test at every change. However, this is very expensive and very time consuming. Automated testing should help reducing testing time by prioritizing tests that have higher likelihood to detect problems and by clustering tests into groups so that it is not necessary to run all the tests in each group. Clustering and prioritization can be done using modern artificial intelligence (AI) algorithms and this is exactly the objective of this project. Therefore, we are going to collect historical data after running test suite in real environment and then to use this data to train the AI models. Our system will be able to perform online learning even after being deployed.This project has several research challenges including development of AI algorithms, dealing with very large and complex software developed by Ericsson, and dealing with multiprocessing embedded system that runs on multiple boards. The system should improve productivity of software developers in Ericsson significantly. In addition, it has potential to improve productivity of any software developers in Canada saving Canadian companies large amount of money.

在大公司中，软件代码库非常庞大，并且变化非常快。团队花费大量时间来运行测试，即使代码中的更改非常小。为了找到导致测试失败的确切更改，开发人员在每次更改时运行每个测试。然而，这是非常昂贵并且非常耗时的。自动化测试应该通过优先考虑更有可能检测到问题的测试以及将测试分为组来帮助减少测试时间，这样就不必运行每组中的所有测试。可以使用现代人工智能 (AI) 算法来完成聚类和优先级排序，这正是该项目的目标。 因此，我们将在真实环境中运行测试套件后收集历史数据，然后使用这些数据来训练人工智能模型。我们的系统即使在部署后也能够进行在线学习。该项目面临多项研究挑战，包括人工智能算法的开发、处理爱立信开发的非常大且复杂的软件以及处理在多个板上运行的多处理嵌入式系统。该系统将显着提高爱立信软件开发人员的生产力。此外，它还有可能提高加拿大所有软件开发人员的生产力，为加拿大公司节省大量资金。