Johannes Dorn

Leipzig University
Chair of Software Systems
Augustusplatz 10
04109 Leipzig


Room: Phone: E-mail:	P624 (Paulinum) +49 (0) 341 97 323 93 jdorn@cs.uni-leipzig.de

Courses

„Conformal Prediction für konfigurierbare Softwaresysteme“, Master, 2024
Comparing Fine-Tuning Techniques for large Language Models for Supporting Development of Individual Software Systems“, Bachelor, 2024
„Explicit Inference of Model Structure Uncertainty in Configurable Software Systems“, Master, ongoing
„Benchmark mining and performance prediction on varying hardware systems using machine learning“, Bachelor, 2023
„Entwicklung eines IDE-Plugins zur Analyse von Software-Entwicklungszyklen“, Master, 2023
„Über den Einfluss der Workload-Variabilität auf die Leistung konfigurierbarer Softwaresysteme: Eine Literaturstudie und empirische Auswertung“ Bachelor, 2022
„Entwicklung und Evaluation eines Active-Learning-Ansatzes auf Basis von Modell-Unsicherheiten“, Bachelor, 2022
„Reproducibility in Search-Based Software Engineering“, Master, 2019
„Generating Realistic Attributed Variability Models via Multi-Objective Optimization“, Master, 2018

2023

Johannes Dorn, Sven Apel, and Norbert Siegmund. Mastering Uncertainty in Performance Estimations of Configurable Software Systems. Empirical Software Engineering (EMSE) 28, 33 (2023).
Stefan Mühlbauer, Florian Sattler, Christian Kaltenecker, Johannes Dorn, Sven Apel, and Norbert Siegmund. Analyzing the Impact of Workloads on Modeling the Performance of Configurable Software Systems. In Proceedings of the International Conference on Software Engineering (ICSE). IEEE, May, 2023. Acceptance rate: 26% (209 / 796), to appear.

2022

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.

2020

Johannes Dorn, Sven Apel, and Norbert Siegmund. Mastering Uncertainty in Performance Estimations of Configurable Software Systems. In Proceedings of Automated Software Engineering (ASE), 2020. Acceptance rate (full papers): 22.5% (93 / 408).
Johannes Dorn, Sven Apel, and Norbert Siegmund. Generating Attributed Variability Models for Transfer Learning. In Proceedings of the 14th International Working Conference on Variability Modelling of Software-Intensive Systems (VaMoS), pages 1–8. ACM, 2020.