Machine Learning for Software User Interface Testing
Tracing functional and non-functional requirements within user interface test cases
At a glance
- Project leader : Dr. Lukas Fievet, Dr. Marcela Ruiz
- Co-project leader : Jose David Mosquera Tobón
- Project status : completed
- Funding partner : Innosuisse (Innovationsscheck / Projekt Nr. 54089.1 INNO-ICT)
- Contact person : Marcela Ruiz
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.
Mosquera, David; Ruiz, Marcela; Pastor, Oscar; Spielberger, Jürgen; Fievet, Lucas,
OntoTrace : a tool for supporting trace generation in software development by using ontology-based automatic reasoning [paper].
De Weerdt, Jochen; Polyvyanyy, Artem, eds.,
Intelligent Information Systems.
34th International Conference on Advanced Information Systems Engineering (CAiSE '22), Leuven, Belgium, 6-10 June 2022.
Lecture Notes in Business Information Processing ; 452.
Available from: https://doi.org/10.1007/978-3-031-07481-3_9