AI Methods for Automated Software Testing

在大公司中，软件代码库非常大，而且变化非常快。团队花费大量时间运行测试，即使是代码中非常小的更改。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，这是非常昂贵且非常耗时的。自动化测试应该有助于减少测试时间，方法是将检测问题的可能性更高的测试划分优先级，并将测试分组，这样就不必运行每个组中的所有测试。聚类和优先级排序可以使用现代人工智能（AI）算法来完成，这正是该项目的目标。 因此，我们将在真实的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AI模型。我们的系统将能够执行在线学习，即使在部署后。这个项目有几个研究挑战，包括人工智能算法的开发，处理由爱立信开发的非常大和复杂的软件，并处理多处理嵌入式系统，运行在多个板。该系统将大大提高爱立信软件开发人员的生产力。此外，它有可能提高加拿大任何软件开发人员的生产力，为加拿大公司节省大量资金。

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.