BIO: Noemi is a software engineer passionate about technology and testing. She has been in and out of testing roles and has always strive for quality, automation and tooling creation to ease the entire development process. She has worked in multinational companies (such IBM, Microsoft and Dell) and also in a startup in Ireland. Currently she has reallocated to China and she is working for Netease games.
Presentation: Using Machine Learning for Test Case Decision
session level: beginner
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.
This talk would provide new ways of automating tasks and would inspire the audience to come up with some of these or use them for their projects.
It would teach the audience some basic knowledge about AI, Rule base system and Machine learning.
It would also open discussions about the future of testing and the ethics of relying on computers to do human tasks.