Prof. Dr. Norbert Siegmund

Chair of Intelligent Software Systems

News:

  • Accepted paper at ASE 2019: "Accurate Modeling of Performance Histories for Evolving Software Systems" with Stefan Mühlbauer and Sven Apel.
  • Teaching award for the best course of the media faculty and receiver of the university's teaching award 2019!  More information >here<
  • Accepted paper at ICSE 2019: "Distance-Based Sampling of Software Configuration Spaces" with Christian Kaltenecker, Alexander Grebhahn, Jianmei Guo, and Sven Apel!
  • Invited keynote at VaMoS 2019!
  • Accepted TSE journal paper: Finding Faster Configurations with Flash with Vivek Nair, Zhe Yu, Tim Menzies, and Sven Apel!
  • PC member at ESEC/FSE 2019 in Tallinn, Estland.
  • Joined the steering committee of VaMoS: The new working conference on variability management and beyond!
  • We will host the next meeting of the feature-oriented software development community in Weimar!
  • Workshop Co-Chair at ESEC/FSE 2019 in Tallinn, Estland. 
  • The German Research Foundation (DFG) funds the new research project "Green Configuration: Understanding the Influence of Software Configurations on Energy Consumption" with 590,000€! 
  • We've got 3 papers accepted at ESEC/FSE, one of the most renowned software engineering conferences (acceptance rate: 24%).
  • First JUG meeting at the Bauhaus-University Weimar attracted 50 people from academia and industry!
  • Norbert Siegmund is co-founder of the Java User Group Thuringia (follow us on Twitter: @jugthde)
  • The German Research Foundation (DFG) invest 289,000€ in funding a cooperative research project together with the University of Passau. The project "Pervolution" will investigate how performance changes during the evolution of highly configurable software systems.

 

 

Research Topics:

The Chair of Intelligent Software Systems develops methods, tools, and theories to improve the construction, maintenance, usability, and optimization of complex, configurable software systems. Our main focus is on the non-functional properties of software systems, such as performance and energy consumption. We combine classical and novel machine learning techniques to solve challenges in the area of software engineering. This relative young branch of software engineering, called "Search-Based Software Engineering", explores the synergies of machine learning and software-engineering methods:

  • Software Engineering fundamentally aims at automatization and optimization in software development and usage. Machine learning techniques can give new insights and solve tasks that are too complex and too big to for the human brain. 
  • Vice versa, also techniques from software engineering, especially from domain of configurable software systems and variant management, can help to maintain and control the ever growing number of increasingly complex machine learning techniques. In this line of research, we also strive for automatization of complex systems and to make them accessible and usable for non-experts in machine learning.