Research

Research:

Software Engineering meets AI

SE 4 AI

How to specify, design, construct, test, and deploy AI-enabled systems?

Investigating how software engineering needs to adapt.

Performance & Energy

How to optimize performance, reduce energy, and find optimal software configurations?

Investigating how software analyses, metaheuristics, sampling techniques, and machine learning helps in improving software efficiency.

AI 4 SE

How to automatically find configuration errors, generate code suggestions, and write code reviews?

Investigating how modern AI and NLP techniques help in automating tedious and error-prone SE tasks.

Performance Data

Several months of 24/7 peroformance measurements of x real-world configurable software systems.
The data set consists variability models describing configuration options and constraints for each system, configurations that have been measured including their measured values.

Energy Data

We invested substantial effort in developing a highly accurate and realiable energy measurement setup. We provide both coarse-grained (system-wise) and fine-grained (component-wise) measurements. We will make the data available upon paper acceptance.

Qualitative Data

We provide transcripts of our interviews regarding configuration aspects, configuration errors, and configuration-related performance issues. The raw interviews have been anomynized.

Tools & Data Sets

We are happy to share our tools and data sets. We would appreciate if the corresponding papers are cited.

https://github.com/se-passau/SPLConqueror

SPL Conqueror: Sampling and learning of non-functional properties of configurable software systems

https://github.com/digital-bauhaus/Loki

Thor: Generating realistic attributed variability models

https://github.com/AI-4-SE

BlackWidow: Automatically detecting inconsistencies in configuration networks across full stack CI/CD pipelines. TBA

SE 4 AI

Agile-AI

Developing novel software-engineering technqiues to support the specification, execution, and management of AI experiments.

BlackWidow

Automatically detecting configuration errors and inconsistencies among a diverse set of configuration files and artifacts, such as Docker files, source code, automation scripts, build scripts.

Christian Kaltenecker, Alexander Grebhahn, Norbert Siegmund, and Sven Apel. The Interplay of Sampling and Machine Learning for Software Performance Prediction. IEEE Software, 2020.

Performance & Energy

Green Configuration

We develop techniques to measure, predict, and optimize the influence of features and their interactions on the energy consumption of a configurable system. Furthermore, we develop methods and analysis techniques to support developers in identifying the cause of energy problems in the source code.

Pervolution

We develop an approach to facilitate performance-aware evolution of complex, configurable systems, by tracking down evolutionary performance changes and by providing development guidelines based on extracted performance-evolution patterns. Our goal is to provide deep insights into the performance evolution of configuration options and their interactions, so that developers can reason about their decisions about the system’s configurability during software evolution, in the light of the changed performance behavior.

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), ACM Press, 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.

Research:

Software Engineering meets AI

SE 4 AI

Performance & Energy

AI 4 SE

Performance Data

Energy Data

Qualitative Data

Tools & Data Sets

We are happy to share our tools and data sets. We would appreciate if the corresponding papers are cited.

https://github.com/se-passau/SPLConqueror

SPL Conqueror: Sampling and learning of non-functional properties of configurable software systems

https://github.com/digital-bauhaus/Loki

Thor: Generating realistic attributed variability models

https://github.com/AI-4-SE

BlackWidow: Automatically detecting inconsistencies in configuration networks across full stack CI/CD pipelines. TBA

SE 4 AI

Agile-AI

Developing novel software-engineering technqiues to support the specification, execution, and management of AI experiments.

BlackWidow

Automatically detecting configuration errors and inconsistencies among a diverse set of configuration files and artifacts, such as Docker files, source code, automation scripts, build scripts.

Christian Kaltenecker, Alexander Grebhahn, Norbert Siegmund, and Sven Apel. The Interplay of Sampling and Machine Learning for Software Performance Prediction. IEEE Software, 2020.

Performance & Energy

Green Configuration

We develop techniques to measure, predict, and optimize the influence of features and their interactions on the energy consumption of a configurable system. Furthermore, we develop methods and analysis techniques to support developers in identifying the cause of energy problems in the source code.

Pervolution

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), ACM Press, 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.

AI 4 SE

Coding AI

Speeding up development time by supporting the programmer with highly accurate code suggestions for code and configuration files.

TBA