Daniel Elsner, M.Sc.
Technische Universität München
Informatik 4 - Lehrstuhl für Software & Systems Engineering (Prof. Pretschner)
85748 Garching b. München
- Tel.: +49 (89) 289 - 17386
Before joining Prof. Pretschner's chair in July 2019, I worked in several positions as software and machine learning engineer. My research focus is on software testing in general and more precisely on regression test optimization in continuous integration environments.
News (October 2021): I'm looking for a highly motivated student with strong background in C++, who is interested in working on code instrumentation with Microsoft Visual C++, Intel PT (processor tracing), DynamoRIO, or Syzygy.
If you are interested in working with us, have a look at our theses openings page or at my currently open topics below (I try to keep them up-to-date).
I am looking for students with a strong background in programming (Java, C/C++) and high motivation to dig deep into regression testing, dynamic/static code analysis, compiler infrastructure, and continuous integration.
In case you have an interesting idea referring to my research areas, please feel free to contact me directly!
|Regression Test Selection for Microsoft Visual C++||Bachelor/Master|
|Regression Test Selection in the JVM||Seminar|
|Regression Test Optimization in Microservices by Linking Distributed Tracing with Code Instrumentation||Master|
|Configuration of Static Analysis Tools for Effective Bug Detection (with Itestra and MS)||Master|
|Test Suite Composition of Open Source Java Projects||Seminar|
|Cost Factors in Software Development Activities (with CQSE)||Master|
|Automating User Acceptance Tests (with VZ)||Bachelor|
|Change-based Test Execution Optimization in the Development Environment (with CQSE)||Bachelor|
|The Cost of Code Reviews||Seminar|
|Methodology to Assess File Accesses of Tests Using System Call Analysis||Seminar|
|Smarter Testing Through Static Analysis||Seminar|
|WS2019/20||Advanced Topics of Software Engineering (IN2309, IN2126)||Lecture + Exercise|
|SS2020||Seminar: Software Quality||Seminar|
|SS2020||Fortgeschrittene Themen des Softwaretests (IN2084)||Lecture + Exercise|
|WS2020/21||Advanced Topics of Software Engineering (IN2309, IN2126)||Lecture + Exercise|
|WS2020/21||Seminar: Software Quality||Seminar|
|SS2021||Seminar: Software Quality||Seminar|
|WS2021/22||Seminar: Software Quality||Seminar|
- Haas, R.*, Elsner, D.*, Juergens, E., Pretschner, A., & Apel, S. (2021). How Can Manual Testing Processes Be Optimized? Developer Survey, Optimization Guidelines, and Case Studies. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 1281-1291).
- Elsner, D., Hauer, F., Pretschner, A., & Reimer, S. (2021). Empirically Evaluating Readily Available Information for Regression Test Optimization in Continuous Integration. In Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (pp. 491-504).
- Maier, M.*, Elsner, D.*, Marouane, C., Zehnle, M., & Fuchs, C. (2019). DeepFlow: Detecting Optimal User Experience From Physiological Data Using Deep Neural Networks. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (pp. 1415-1421).
- Elsner, D., Langer, S., Ritz, F., Müller, R., & Illium, S. (2019). Deep Neural Baselines for Computational Paralinguistics. In Proceedings of Interspeech (pp. 2388-2392).
- Maier, M., Marouane, C., & Elsner, D. (2019). DeepFlow: Detecting Optimal User Experience From Physiological Data Using Deep Neural Networks - Extended Abstract. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (pp. 2108-2110).
- Elsner, D., Aleatrati Khosroshahi, P., MacCormack, A. D., & Lagerström, R. (2019). Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications. In Proceedings of the 52nd Hawaii International Conference on System Sciences (pp. 5827-5836).
- Hawlitschek, F., Kranz, T. T., Elsner, D., Fritz, F., Mense, C., Müller, M. B., & Straub, T. (2017). Sharewood-Forest–A Peer-to-Peer Sharing Economy Platform for Wild Camping Sites in Germany. Hohenheim Discussion Papers in Business, Economics and Social Sciences, 265.