Software Testing Laboratory

Testing of embedded software, empirical studies of software testing, test automation and model-based testing.



Daniel Sundmark




Wasif Afzal



The Software Testing Laboratory (STL) focuses on contemporary and future challenges in testing of embedded software systems, primarily in research projects conducted in close collaboration with industrial partners.

With an emphasis on method and tool development, as well as industrial and practical real life case studies, our research focus includes (but is not limited to) test design, model-based testing, search-based software testing, decision-support for software testing, and test automation. In short, we develop, refine, and evaluate methods, theories and tools for testing of industrial software systems.

The objectives of STL are to improve the current body of knowledge in software testing, and to disseminate our results with the broader research community as well as with our industrial partners. To achieve this, we regularly publish in the main software testing venues, try to keep an open mind, and continuously engage in diverse academic and industrial collaboration.

Ongoing research projects

The overall objective of this project is to research and develop a workflow to speed-up the software release of CPSoS in operation while guaranteeing its reliability.

Project manager at MDH: Wasif Afzal

Main financing: EU

ITS-EASY is an industrial research school in Embedded Software and Systems, affiliated with the School of Innovation, Design and Engineering (IDT) at Mälardalen University (MDH), as an integrated part of the MDH strategic research area Embedded Systems (ES).

Project manager at MDH: Kristina Lundqvist

Main financing: The Knowledge Foundation

The TESTOMAT project will support software teams to strike the right balance by increasing the development speed without sacrificing quality.

Project manager at MDH: Björn Lisper

Main financing: Vinnova, Itea3

TRUSTCM aims at developing a new decision-making system based on machine learning (ML) and data analytics that can be trusted by providing meaningful explanations of the decisions made. This can be achieved either by improving the structure of the ML model or augmenting the training data with semantic explanations

Project manager at MDH: Daniel Sundmark

VeriDevOps is about fast, flexible system engineering that efficiently integrates development, delivery, and operations, thus aiming at quality deliveries with short cycle time to address ever evolving challenges.

Project manager at MDH: Gunnar Widforss

Main financing: European Commission Horizon 2020

XIVT - eXcellence in Variant Testing

Project manager at MDH: Eduard Paul Enoiu

Main financing: Itea3