Department of Computer Science
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- 2023
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Mark
My Space, Our Space, Their Space: a First Glance at Developers' Experience of Spaces
(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
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Mark
Programmer Stories, Stories for Programmers: Exploring Storytelling in Software Development
(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
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Mark
Efficient Order Batching Optimization Using Seed Heuristics and the Metropolis Algorithm
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- Contribution to journal › Article
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Mark
EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text
2023)(
- Working paper/Preprint › Preprint in preprint archive
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Mark
Building Stronger Bridges: Strategies for Improving Communication and Collaboration Between Industry and Academia in Software Engineering
2023)(
- Thesis › Doctoral thesis (compilation)
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Mark
On the Challenges of Software Performance Optimization with Statistical Methods
2023)(
- Thesis › Doctoral thesis (compilation)
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Mark
Graphs with many edge-colorings such that complete graphs are rainbow
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- Contribution to journal › Article
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Mark
Object Detector Differences when Using Synthetic and Real Training Data
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- Contribution to journal › Article
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Mark
Classification-based Static Collection Selection for Java: Effectiveness and Adaptability
2023) p.111-120(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
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Mark
Performance Analysis with Bayesian Inference
2023) The 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
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Mark
Towards optimization of anomaly detection in DevOps
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- Contribution to journal › Article
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Mark
On parallel time in population protocols
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- Contribution to journal › Article
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Mark
Mutation Testing: Fewer, Faster, and Smarter
2023)(
- Thesis › Doctoral thesis (compilation)
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Mark
Pipeline Infrastructure Required to Meet the Requirements on AI
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- Contribution to journal › Article
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Mark
Ergo, SMIRK is safe : a safety case for a machine learning component in a pedestrian automatic emergency brake system
(
- Contribution to journal › Article