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- 2024
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Mark
An Empirically Grounded Path Forward for Scenario-Based Testing of Autonomous Driving Systems
2024) 32nd ACM International Conference on the Foundations of Software Engineering, FSE Companion In FSE Companion - Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering p.232-243(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
- 2023
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Mark
An empirically based model of software prototyping : a mapping study and a multi-case study
(
- Contribution to journal › Article
-
Mark
Software selection in large-scale software engineering: A model and criteria based on interactive rapid reviews
2023) In Empirical Software Engineering(
- Contribution to journal › Article
-
Mark
Message from the Chairs
2023) 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2023(
- Contribution to conference › Other
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Mark
Svensk forskningspolitik måste uppdateras
2023) In Upsala Nya Tidning(
- Contribution to specialist publication or newspaper › Newspaper article
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Mark
Initial Case Study Findings for Requirements on Work-Related Health Aspects
2023) 31st IEEE International Requirements Engineering Conference Workshops, REW 2023 In Proceedings - 31st IEEE International Requirements Engineering Conference Workshops, REW 2023 p.388-396(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
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Mark
U Owns the Code That Changes and How Marginal Owners Resolve Issues Slower in Low-Quality Source Code
2023) p.368-377(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
-
Mark
Industry-academia collaboration for realism in software engineering research : Insights and recommendations
(
- Contribution to journal › Article
-
Mark
Applying Machine Learning to Gaze Data in Software Development: a Mapping Study
2023) Eleventh International Workshop on Eye Movements in Programming, EMIP 2023(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
-
Mark
Ergo, SMIRK is safe : a safety case for a machine learning component in a pedestrian automatic emergency brake system
(
- Contribution to journal › Article