Emelie Engström
11 – 20 of 64
- show: 10
- |
- sort: year (new to old)
Close
Embed this list
<iframe src=""
width=""
height=""
allowtransparency="true"
frameborder="0">
</iframe>
- 2024
-
Mark
Advancing Software Monitoring : An Industry Survey on ML-Driven Alert Management Strategies
(2024) 50th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2024 p.435-442
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
-
Mark
Revisiting the construct and assessment of industrial relevance in software engineering research within
(2024) International Workshop on Methodological Issues with Empirical Studies in Software Engineering, WSESE 2024 p.17-20
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
-
Mark
Industrial adoption of machine learning techniques for early identification of invalid bug reports
- Contribution to journal › Article
-
Mark
Industry Practices for Challenging Autonomous Driving Systems with Critical Scenarios
- Contribution to journal › Article
-
Mark
Experiences from conducting rapid reviews in collaboration with practitioners — Two industrial cases
- Contribution to journal › Article
-
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
-
Mark
Adopting automated bug assignment in practice — a longitudinal case study at Ericsson
- Contribution to journal › Article
- 2023
-
Mark
Threats to validity in software engineering research: A critical reflection
(2023) In Information and Software Technology
- Contribution to journal › Article
-
Mark
A data-driven approach for understanding invalid bug reports: An industrial case study
- Contribution to journal › Article
-
Mark
Towards optimization of anomaly detection in DevOps
- Contribution to journal › Article
