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Machine Learning for Software User Interface Testing

Tracing functional and non-functional requirements within user interface test cases

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In this project, various technical aspects for tracing functional/non-functional requirements within user interface test cases were investigated. Originally, this project aimed to detect bugs in user interfaces automatically by using machine learning algorithms. During the execution of the project, we decided to replace the machine learning algorithms by other Artificial Intelligence technique that proved to be more successful: an automatic reasoner based on ontologies. Such automatic reasoner allows testers for detecting inconsistencies—that could lead to bugs—between functional/non-functional requirements and user interfaces test cases generated by LogicFlow AG platform.