Using Machine Learning for Test Case Decision
Running too many tests could be expensive in the agile world. Selecting the right test cases to run has always been a tough task.
In this talk, I intend to explain how machine learning could help us determine test cases to run from a large test suite. I start explaining the problem itself and the variables that we can take into account in order to decide the most representative tests.
We will explore examples of situations of data relationships that would not seem to be obvious for a human but a machine could detect.
Then I explain a bit on machine learning and how it could be applied to this problem. We will look into how much reliable this solution would be and what can we do to implement it, as well as alternatives to this solution.
Last I show an example and open for questions.
Outline/Structure of the Demonstration
- What are we trying to solve?
- Examples of the problem and how do we currently solve it
- Rule-based system
- Machine learning solution
- This talk would provide new ways of automating tasks and would inspire the audience.
- It would also open discussions about the future of testing and the ethics of relying on computers to do human tasks.
Anybody interested in AI, ML or in improving their efficiency for test case execution
Prerequisites for Attendees
No previous requirements, I think all levels could get something interesting out of it.