AI Methods for Automated Software Testing

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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.