Norbert Siegmund,

Prof. Dr.

Chair of Software Systems
Leipzig University

Curriculum vitae, short CV, bio, DBLP, Google Scholar, ACM, ORCiD, Twitter

Coordinates

Leipzig University
Institute of Computer Science
Augustusplatz 10, Room Paulinum 601
04109 Leipzig

News

Announcing the first German software engineering podcast for bachelor students

Research

Software engineering and artificial intelligence are more and more intertwined disciplines. My research is driven by the quest how software engineering can be automated and enriched with methods from AI and how intelligent systems can be built, maintained, and evolve with methods from software engineering.

Specifically, my focus is on:

Configurable, complex systems that evolve over time
Non-functional properties, such as performance and energy consumption
Sampling, learning, and optimization methods from AI
Contemporary empirical methods in software engineering
MLOps and DevOps
Roles, organisation, and social aspects of AI-production teams

I apply and validate my methods on real-world software systems, interview experts, and collect and analyze industrial practices to develop common theories, models, and tools.

Awards & Honors

Keynote „Modelling the Universe: Accurate & Interpretable Performance Models for an Astronomical Number of Influences“ at QAVS (2021)
Open-Source Award of the state Thuringia for a charity project combining industry experts and students (2020)
Best Lecture Award at Media Department of the Bauhaus-Universität Weimar (2019)
Keynote „Challenges and Insights from Optimizing Configurable Software Systems“ at VaMoS
ACM SIGSOFT Distinguished Paper Award at the 37th International Conference on Software Engineering (2015)
Best PhD in 2013 of the faculty of computer science at University of Magdeburg (2013)
Award for the innovative teaching concept: „explorative and interactive learning“ (2012)
Das innovativen Lehrkonzept „Exploratives und interaktives Lernen“ gewann den mit 5.000 Euro dotierten Preis des Projekts fokus: Lehre der Otto-von-Guericke-Universität Magdeburg.
SPLC’s Best Paper Award (2012)
FIN Forschungspreis – spezielle Arbeit (faculty research award – special paper) (2012)

Grants & Projects

Green Configuration: Determining the Influence of Software Configurations on Energy Consumption (DFG, 590,000 €, 2017-2022)
Coding-AI: An AI for Code Suggestions (BMBF,180,000 € as part of ScaDS.AI, Leipzig, 2019-2022)
Pervolution: Performance Evolution of Configurable Software Systems (DFG, 290,000 € of 580,000 €, with Co-PI Prof. Sven Apel, 2017-2020)
Agile-AI: Agile Entwicklung von Systemen der Künstlichen Intelligenz (BMBF, 181,000 € of 711,475 €, with Co-PI Prof. Benno Stein and Co-PI Prof. Martin Potthast, 2019-2022)

Teaching

Software Engineering
(Bachelor)

Software Engineering for AI-Enabled Systems (Master)

Configurable Software Systems (Bachelor/Master)

Software Engineering Project (Bachelor)

Automated Software Engineering (Master)

Introduction to Programming in Java (Bachelor)

Services
(selected)

Steering Committee at VaMoS (since 2018)

PC at ESEC/FSE (2019, 2020)

PC at ICPE (2022)

Workshop Chair at ESEC/FSE (2019)

TSE review board (2018-19)

PC at ASE (2016, 2018)

PC at SPLC (2016, 2017, 2020)

Program Chair at VaMoS (2017)

Track Chair at SPLC (2016)

Program Chair at FOSD (2013)

Publicity Chair at GPCE (2013)

Selected Publications

Full publications

Miguel Velez, Pooyan Jamshidi, Norbert Siegmund, Sven Apel, and Christian Kästner. On Debugging the Performance of Configurable Software Systems: Developer Needs and Tailored Tool Support. In Proceedings of the International Conference on Software Engineering (ICSE). ACM, May 2022. Acceptance rate: 26% (197 / 751). To appear.
Norbert Siegmund, Johannes Dorn, Max Weber, Christian Kaltenecker, and Sven Apel. Green Configuration: Can AI Help in Reducing Energy Consumption of Configurable Software Systems? IEEE Computer, Volume 55 , Issue 3, 2022.
Max Weber, Sven Apel, and Norbert Siegmund. White-Box Performance-Influence Models: A Profiling and Learning Approach. In Proceedings of the International Conference on Software Engineering (ICSE). IEEE, May, 2021. Acceptance rate: 23% (138 / 602).
Miguel Velez, Pooyan Jamshidi, Norbert Siegmund, Sven Apel, and Christian Kästner. White-Box Analysis over Machine Learning: Modeling Performance of Configurable Systems. In Proceedings of the International Conference on Software Engineering (ICSE). IEEE, May 2021. Acceptance rate: 23% (138 / 602).
Alexander Grebhahn, Christian Kaltenecker, Christian Engwer, Norbert Siegmund, Sven Apel. Lightweight, Semi-Automatic Variability Extraction: A Case Study on Scientific Computing. Empirical Software Engineerin (ESE), 2020. To appear.
Stefan Mühlbauer, Sven Apel, and Norbert Siegmund. Identifying Software Performance Changes Across Variants and Versions. Automated Software Engineering (ASE), pages 611–622. ACM, 2020. Acceptance rate (full papers): 22.5% (93 / 408).
Johannes Dorn, Sven Apel, and Norbert Siegmund. Mastering Uncertainty in Performance Estimations of Configurable Software Systems. In Proceedings of Automated Software Engineering (ASE), pages 684–696. ACM, 2020. Acceptance rate (full papers): 22.5% (93 / 408).
Norbert Siegmund, Nicolai Ruckel, and Janet Siegmund. Dimensions of Software Configuration. In Proceedings of the European Software Engineering Conference and the ACM SIGSOFT International Symposium on the Foundations of Software Engineering (ESEC/FSE), pages 338–349. ACM Press, 2020. Acceptance rate (full papers): 28% (101 / 360).
Christian Kaltenecker, Alexander Grebhahn, Norbert Siegmund, and Sven Apel. The Interplay of Sampling and Machine Learning for Software Performance Prediction. IEEE Software, 37(4):58–66, 2020.
Vivek Nair, Zhe Yu, Tim Menzies, Norbert Siegmund, and Sven Apel. Finding Faster Configurations using FLASH. IEEE Transactions on Software Engineering (TSE), 46(7): 794-811, 2020.
Stefan Mühlbauer, Sven Apel, and Norbert Siegmund. Accurate Modeling of Performance Histories for Evolving Software Systems. In Proceedings of the IEEE/ACM International Conference on Automated Software Engineering (ASE), pages 640–652. IEEE Computer Society, 2019. Acceptance rate (full papers): 24% (91 / 373).
Christian Kaltenecker, Alexander Grebhahn, Norbert Siegmund, Jianmei Guo, and Sven Apel. Distance-Based Sampling of Software Configuration Spaces. In Proceedings of the International Conference on Software Engineering (ICSE), pages 1084–1094. ACM, 2019. Acceptance rate: 21% (109 / 529).
Pooyan Jamshidi, Miguel Velez, Christian Kästner, and Norbert Siegmund. Learning to Sample: Exploiting Similarities Across Environments to Learn Performance Models for Configurable Systems. In Proceedings of the European Software Engineering Conference and the ACM SIGSOFT International Symposium on the Foundations of Software Engineering (ESEC/FSE), pages 71-82, ACM Press, 2018.
Pooyan Jamshidi, Norbert Siegmund, Miguel Velez, Christian Kästner, Akshay Patel, and Yuvraj Agarwal. Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis. In Proceedings of the International Conference on Automated Software Engineering (ASE), pages 497-508,ACM Press, November 2017. Acceptance rate: 21% (65 / 314).
Norbert Siegmund, Stefan Sobernig, and Sven Apel. Attributed Variability Models: Outside the Comfort Zone. In Proceedings of the European Software Engineering Conference and the ACM SIGSOFT International Symposium on the Foundations of Software Engineering (ESEC/FSE), pages 268 – 278, ACM Press, September 2017. Acceptance rate: 24% (72 / 295).
Jeho Oh, Don Batory, Margaret Myers, and Norbert Siegmund. Finding Near-Optimal Configurations in Product Lines by Random Sampling. In Proceedings of the European Software Engineering Conference and the ACM SIGSOFT International Symposium on the Foundations of Software Engineering (ESEC/FSE), pages 61 – 71, ACM Press, September 2017. Acceptance rate: 24% (72 / 295).
Vivek Nair, Tim Menzies, Norbert Siegmund, and Sven Apel. Using Bad Learners to find Good Configurations. In Proceedings of the European Software Engineering Conference and the ACM SIGSOFT International Symposium on the Foundations of Software Engineering (ESEC/FSE), pages 257 – 267, ACM Press, September 2017. Acceptance rate: 24% (72 / 295).
Pooyan Jamshidi, Miguel Velez, Christian Kästner, Norbert Siegmund, and Prassad Kawthekar.Transfer Learning for Improving Model Predictions in Highly Configurable Software. In Proceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), pages 31-41, IEEE, May 2017. Acceptance rate: 23%.

Atri Sarkar, Jianmei Guo, Norbert Siegmund, Sven Apel, and Krzysztof Czarnecki. Cost-Efficient Sampling for Performance Prediction of Configurable Systems. In Proceedings of the IEEE/ACM International Conference on Automated Software Engineering (ASE), pages 342–352. IEEE Computer Society, November 2015. Acceptance rate (full papers): 21% (60 / 289).
Andreas Wölfl, Norbert Siegmund, Sven Apel, Harald Kosch, Johann Krautlager, and Guillermo Weber-Urbina. Generating Qualifiable Avionics Software: An Experience Report. In Proceedings of the IEEE/ACM International Conference on Automated Software Engineering (ASE), pages 726–736. IEEE Computer Society, November 2015. Acceptance rate (full papers): 21% (60 / 289).
Norbert Siegmund, Alexander Grebhahn, Sven Apel, and Christian Kästner. Performance-Influence Models for Highly Configurable Systems. In Proceedings of the European Software Engineering Conference and the ACM SIGSOFT International Symposium on the Foundations of Software Engineering (ESEC/FSE), pages 284–294. ACM Press, August 2015. Acceptance rate: 25% (74 / 291).
Janet Siegmund, Norbert Siegmund, and Sven Apel. Views on Internal and External Validity in Empirical Software Engineering. In Proceedings of the IEEE/ACM International Conference on Software Engineering (ICSE), pages 9–19. IEEE Computer Society, May 2015. Acceptance rate: 19% (84 / 452); ACM SIGSOFT Distinguished Paper Award.
Alexander von Rhein, Alexander Grebhahn, Sven Apel, Norbert Siegmund, Dirk Beyer, and Thorsten Berger. Presence-Condition Simplification in Highly Configurable Systems. In Proceedings of the IEEE/ACM International Conference on Software Engineering (ICSE), pages 178–188. IEEE Computer Society, May 2015. Acceptance rate: 19% (84 / 452).
Norbert Siegmund, Sergiy Kolesnikov, Christian Kästner, Sven Apel, Don Batory, Marko Rosenmüller, and Gunter Saake. Predicting Performance via Automated Feature-Interaction Detection. In Proceedings of the IEEE/ACM International Conference on Software Engineering (ICSE), pages 167–177. IEEE Computer Society, June 2012. Acceptance rate: 21% (87 / 408).