no code implementations • 30 Jan 2024 • Jens Henriksson, Christian Berger, Stig Ursing, Markus Borg
Safety measures need to be systemically investigated to what extent they evaluate the intended performance of Deep Neural Networks (DNNs) for critical applications.
no code implementations • 10 Mar 2023 • Hans-Martin Heyn, Khan Mohammad Habibullah, Eric Knauss, Jennifer Horkoff, Markus Borg, Alessia Knauss, Polly Jing Li
An industry of data and annotation services has emerged to serve the development of such data-intensive automotive software components.
no code implementations • 13 Oct 2022 • John Pavlopoulos, Alv Romell, Jacob Curman, Olof Steinert, Tony Lindgren, Markus Borg
Automated fault diagnosis can facilitate diagnostics assistance, speedier troubleshooting, and better-organised logistics.
no code implementations • 26 Apr 2022 • Jens Henriksson, Christian Berger, Markus Borg, Lars Tornberg, Sankar Raman Sathyamoorthy, Cristofer Englund
Implementing Deep Neural Networks (DNN) for non-safety related applications have shown remarkable achievements over the past years; however, for using DNNs in safety critical applications, we are missing approaches for verifying the robustness of such models.
1 code implementation • 16 Apr 2022 • Markus Borg, Jens Henriksson, Kasper Socha, Olof Lennartsson, Elias Sonnsjö Lönegren, Thanh Bui, Piotr Tomaszewski, Sankar Raman Sathyamoorthy, Sebastian Brink, Mahshid Helali Moghadam
We initiated a research project with the goal to demonstrate a complete safety case for an ML component in an open automotive system.
no code implementations • 30 Mar 2022 • Qunying Song, Markus Borg, Emelie Engström, Håkan Ardö, Sergio Rico
Four researchers from Lund University and RISE Research Institutes and four practitioners from Axis Communications reviewed a set of 180 primary studies on ML testing.
no code implementations • 29 Mar 2022 • Markus Borg, Johan Bengtsson, Harald Österling, Alexander Hagelborn, Isabella Gagner, Piotr Tomaszewski
Due to the migration megatrend, efficient and effective second-language acquisition is vital.
no code implementations • 22 Mar 2022 • Mahshid Helali Moghadam, Markus Borg, Mehrdad Saadatmand, Seyed Jalaleddin Mousavirad, Markus Bohlin, Björn Lisper
This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system.
no code implementations • 28 Nov 2021 • Markus Borg
Artificial intelligence through machine learning is increasingly used in the digital society.
no code implementations • 16 Nov 2021 • Piotr Tomaszewski, Shun Yu, Markus Borg, Jerk Rönnols
Small angle X-ray scattering (SAXS) is extensively used in materials science as a way of examining nanostructures.
1 code implementation • 16 Sep 2021 • Hamid Ebadi, Mahshid Helali Moghadam, Markus Borg, Gregory Gay, Afonso Fontes, Kasper Socha
This paper presents the results of our proposed test generation technique in the 2021 IEEE Autonomous Driving AI Test Challenge.
no code implementations • 29 Mar 2021 • Jens Henriksson, Christian Berger, Markus Borg, Lars Tornberg, Sankar Raman Sathyamoorthy, Cristofer Englund
Understanding the relationship between training results and supervisor performance is valuable to improve robustness of the model and indicates where more work has to be done to create generalized models for safety critical applications.
no code implementations • 4 Mar 2021 • Markus Borg, Joshua Bronson, Linus Christensson, Fredrik Olsson, Olof Lennartsson, Elias Sonnsjö, Hamid Ebabi, Martin Karsberg
Moreover, bigger questions related to societal and environmental impact cannot be tackled by an ADAS supplier in isolation.
no code implementations • 2 Mar 2021 • Markus Borg, Ronald Jabangwe, Simon Åberg, Arvid Ekblom, Ludwig Hedlund, August Lidfeldt
In this paper, we demonstrate how Grad-CAM heatmaps can be used to increase the explainability of an image recognition model trained for a pedestrian underpass.
no code implementations • 12 Dec 2020 • Markus Borg, Raja Ben Abdessalem, Shiva Nejati, Francois-Xavier Jegeden, Donghwan Shin
Based on a minimalistic scene, we compare critical test scenarios generated using our SBST solution in these two simulators.
no code implementations • 11 Sep 2020 • Markus Borg
In this paper, we share our working definition and a pragmatic approach to address the corresponding quality assurance with a focus on testing.
no code implementations • 11 Sep 2020 • August Lidfelt, Daniel Isaksson, Ludwig Hedlund, Simon Åberg, Markus Borg, Erik Larsson
One promising approach for smart cameras is edge AI, i. e., deploying AI technology on IoT devices.
1 code implementation • 19 Aug 2019 • Mahshid Helali Moghadam, Mehrdad Saadatmand, Markus Borg, Markus Bohlin, Björn Lisper
On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learned by the testing system, then test automation without advanced performance models could be possible.
no code implementations • 13 Aug 2019 • Andreas Vogelsang, Markus Borg
We conclude that development of ML systems demands requirements engineers to: (1) understand ML performance measures to state good functional requirements, (2) be aware of new quality requirements such as explainability, freedom from discrimination, or specific legal requirements, and (3) integrate ML specifics in the RE process.
2 code implementations • 5 Mar 2019 • Markus Borg, Oscar Svensson, Kristian Berg, Daniel Hansson
We present SZZ Unleashed, an open implementation of the SZZ algorithm for git repositories.
Software Engineering
no code implementations • 4 Mar 2019 • Jens Henriksson, Christian Berger, Markus Borg, Lars Tornberg, Cristofer Englund, Sankar Raman Sathyamoorthy, Stig Ursing
Deep Neural Networks (DNN) have improved the quality of several non-safety related products in the past years.
no code implementations • 26 Apr 2017 • Markus Borg, Iben Lennerstad, Rasmus Ros, Elizabeth Bjarnason
In a classification task with limited resources, Active Learning (AL) promises to guide annotators to examples that bring the most value for a classifier.