Scope of the Workshop
System variants often arise by configuring parameters that have a direct impact on the system’s behavior. Most prominently, in feature-oriented system design, features describe optional or incremental system functionalities whose configuration is simply whether a feature is active or inactive. Since the configuration space usually suffers from an exponential blowup in the number of configuration parameters, such variant-rich systems require specialized methods for their design, implementation, and analysis. Quantitative aspects such as probability of failure, energy consumption, or also numerical parameter values gain more and more attention due to the rising impact of co-adaptive and autonomous cyber-physical systems using, e.g., modern 5G technologies and robotics. While there are well-developed methodologies for variant-rich systems that do not take quantitative specifications into account, research on quantitative aspects is still done in fairly isolated branches. The main goal of this workshop is to bring researchers of the field together and foster their collaboration, presenting the different approaches to deal with non-functional properties of variant-rich systems.
Workshop Format and Topics of Interest
The workshop comprises invited presentations, talks based on papers submitted following the call for papers, and presentation-only submissions related but not limited to the following topics:
As the main conference ETAPS 2021, the workshop takes place without physical meetings and will be conducted online. Technical details will follow and be presented on this webpage.
Submission DetailsWe solicit three kinds of submissions:
- Regular papers describing original research results or surveys. Such papers should not exceed 12 pages excluding references.
- Short papers describing experiences, case studies, tools, work in progress, or exploratory ideas. Such papers should not exceed 6 pages excluding references.
- Presentation-only submissions comprise an abstract that describes the tentative content of the talk. Such abstracts should not exceed 2 pages and may include already published material, unpublished work, and even challenges.
All regular paper and short paper submissions must be original, unpublished, and not submitted concurrently for publication elsewhere. Artifacts required to judge the paper should be made available through an URL. Paper submission is done via EasyChair. All submissions must be written in English and formatted according to the guidelines for EPTCS papers (see http://info.eptcs.org). Accepted papers have to be presented at the workshop and a full version has to be submitted to be published in the EPTCS workshop series.
The following deadlines are 23:59 AoE:
Submission1 March 2021
Notification14 March 2021
Workshop26 March 2021
Camera-ready26 April 2021
Maurice ter Beek
Erik de Vink
Alberto Lluch Lafuente
|16:05||Aleksandar Dimovski||A Decision Tree Lifted Domain for Analyzing Program Families with Numerical Features|
|16:35||Philipp Chrszon||Role-based Automata: Modeling and Formal Analysis of Context-Dependent Systems|
|17:10||Davide Basile||Static Detection of Equivalent Mutants in Real-Time Model-based Mutation Testing|
|17:40||Virtual coffee break|
|18:00||Norbert Siegmund||Keynote Modelling the Universe: Accurate & Interpretable Performance Models for an Astronomical Number of Influences|
Norbert SiegmundUniversity 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.