Second Workshop on

Quantitative Aspects of Variant-rich Systems

26 March 2021, colocated with ETAPS 2021


CET Speaker Topic
16:05Aleksandar Dimovski A Decision Tree Lifted Domain for Analyzing Program Families with Numerical Features
16:35Philipp Chrszon Role-based Automata: Modeling and Formal Analysis of Context-Dependent Systems
17:10Davide Basile Static Detection of Equivalent Mutants in Real-Time Model-based Mutation Testing
17:40Virtual coffee break
18:00Norbert Siegmund Keynote Modelling the Universe: Accurate & Interpretable Performance Models for an Astronomical Number of Influences
18:45Closing discussion

Keynote Speaker

Norbert Siegmund

Norbert Siegmund

University of Leipzig, Germany

"Modelling the Universe: Accurate & Interpretable Performance Models for an Astronomical Number of Influences"

Abstract. Nowadays, nearly all software systems provide configuration capabilities to the user that enable to tune quantitative aspects of the system, such as performance and energy consumption. However, due to the exponential number of configurations rising from the available configuration options, developers, administrators, and users alike are overwhelmed by an astronomical number of possible influences affecting the system's properties. To support the selection of suitable and optimal configurations, several sampling and learning approaches have been proposed in recent years to tame the complexity of the configuration space. In this talk, I will discuss characteristics of performance and how it affects learning of a performance influence model. I will show that different learning techniques have distinct benefits and drawbacks and especially discuss the tension between accuracy, interpretability, and correctness. Finally, I give some ideas on how to address the scalability problem of the exponential configuration space for learning.

5G Lab Germany