About

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.

Thesis Supervision

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++, Rust) 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!

 

Teaching

 

Publications

• Elsner, D., Kacianka, S., Lipp, S., Pretschner, A., Habermann, A., Graber, M., Reimer, S. (2023). BinaryRTS: Cross-language Regression Test Selection for C++ Binaries in CI. In Proceedings of the 16th IEEE International Conference on Software Testing, Verification and Validation (accepted for publication).
• Hundsdorfer, S.*, Elsner, D.*, Pretschner, A. (2023). DIRTS: Dependency Injection Aware Regression Test Selection. In Proceedings of the 16th IEEE International Conference on Software Testing, Verification and Validation (accepted for publication).
• Wuersching, R.*, Elsner, D.*, Leinen, F., Pretschner, A., Grueneissl, G., Neumeyr, T., Vosseler, T. (2023). Severity-Aware Prioritization of System-Level Regression Tests in Automotive Software. In Proceedings of the 16th IEEE International Conference on Software Testing, Verification and Validation (accepted for publication).
• Elsner, D., Wuersching, R., Schnappinger, M., Pretschner, A. (2022). Probe-based Syscall Tracing for Efficient and Practical File-level Test Traces. In Proceedings of the 3rd ACM/IEEE International Conference on Automation of Software Test (pp. 126-137).
• Elsner, D., Bertagnolli, D., Pretschner, A., Klaus, R. (2022). Challenges in Regression Test Selection for End-to-End Testing of Microservice-based Software Systems. In Proceedings of the 3rd ACM/IEEE International Conference on Automation of Software Test (pp. 1-5).
• Lipp, S., Elsner, D., Hutzelmann, T., Banescu, S., Pretschner, A., & Böhme, M. (2022). FuzzTastic: A Fine-grained, Fuzzer-agnostic Coverage Analyzer. In Proceedings of the 44th IEEE/ACM International Conference on Software Engineering Companion (pp. 75-79).
• Elsner, D., Wuersching, R., Schnappinger, M., Pretschner, A., Graber, M., Dammer, R., & Reimer, S. (2022). Build System Aware Multi-language Regression Test Selection in Continuous Integration. In Proceedings of the 44th IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (pp. 87-96).
• 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.