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Research archive

Completed research projects for the research area Software Testing Laboratory (11)

Research project

Project manager at MDH: Daniel Sundmark


Research area: Software Testing Laboratory


Main financing: ITEA 2


A project to develop, enhance, and deploy high performance methods and tools for quality assurance of large and distributed software-intensive systems.

Research project

Project manager at MDH: Cristina Seceleanu


Research area: Formal Modelling and Analysis of Embedded Systems, Software Testing Laboratory


Main financing: The Swedish Research Council


In this project, the overall goal is to develop models and methods for adequacy-based testing of extra-functional properties in embedded systems.

Research project

Project manager at MDH: Maria Lindén


Research area: Artificial Intelligence och Intelligent Systems, Biomedical Engineering, Data Communication, Software Testing Laboratory


Main financing: The Swedish Knowledge Foundation


The goal of the ESS-H profile is that academia and industry together achieve a significant increase in research in the area of embedded sensor systems for health performed at Mälardalen University and participating companies, and that ESS-H is established as a nationally leading and an internationally renowned research center for embedded sensor systems for health.

Research project

Project manager at MDH: Daniel Sundmark


Research area: Software Testing Laboratory


Main financing: Vinnova


The project will focus on making product integration testing more efficient and effective by model based test automation and test reuse.

Research project

Project manager at MDH: Cristina Seceleanu


Research area: Formal Modelling and Analysis of Embedded Systems, Software Testing Laboratory


Main financing: Artemis


MBAT will provide Europe with a new leading-edge Reference Technology Platform for effective and cost-reducing validation and verification, focussing primarily on transportation domain, but also to be used in further domains.

Research project

Project manager at MDH: Wasif Afzal


Research area: Software Testing Laboratory


Main financing: Vinnova


MegaM@Rt brings model-based engineering to the next level in order to help European industry reducing development and maintenance costs while reinforcing both productivity and quality.

Research project

Project manager at MDH: Hans A Hansson


Research area: Real-Time Systems Design, Software Testing Laboratory


Main financing: The Knowledge Foundation


Through the PROMPT project, we intend to establish a national training initiative with the goal of ensuring the supply of software-related advanced skills and innovation to Swedish industry.

Research project

Project manager at MDH: Hans A Hansson


Research area: Complex Real-Time Embedded Systems, Dependable Software Engineering, Industrial Software Engineering, Programming Languages, Real-Time Systems Design, Safety-Critical Engineering, Software Testing Laboratory


Main financing: The Swedish Foundation for Strategic Research


SYNOPSIS is targeting increased efficiency and reduced time-to-market by composable safety certification of safety-relevant embedded systems.

Research project

Project manager at MDH: Wasif Afzal


Research area: Software Testing Laboratory


Main financing: The Knowledge Foundation


In TestMine, we will propose and validate novel techniques that are not dependent on code-coverage information but rather harness test evolution data for RTS at system test.

Research project

Project manager at MDH: Björn Lisper


Research area: Software Testing Laboratory, Programming Languages


Main financing: Vinnova, Itea3


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

Research project

Project manager at MDH: Daniel Sundmark


Research area: Product and Production Development , Software Testing Laboratory


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

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