Variability is an inherent property of software systems that allows developers to deal with the needs of different customers and environments, creating a family of related systems. Variability can be managed in an opportunistic fashion, for example, using clone-and- own, or by employing a systematic approach, for instance, using a software product line (SPL). In the SPL community, variability management has been discussed for systems in various domains, such as defense, avionics, or finance, and for different platforms, such as desktops, web applications, or embedded systems. Unfortunately, other research communities—particularly those working on modern technologies, such as microservice architectures, cyber-physical systems, robotics, cloud computing, autonomous driving, or ML/AI- based systems—are less aware of the state-of-the-art in variability management, which is why they face similar problems and start to redevelop the same solutions as the SPL community already did. This workshop aims to foster and strengthen synergies between the communities researching variability management and modern technologies. More precisely, we intend to attract researchers and practitioners to contribute processes, techniques, tools, empirical studies, and problem descriptions or solutions that are related to reuse and variability management for modern technologies. By inviting different communities and establishing collaborations between them, we hope that the workshop can raise the interest of researchers outside the SPL community for variability management, and thus reduce the extent of costly redevelopments in research.
Gökhan Kahraman and Loek Cleophas: "A Tool for Modeling and Analysis of Relationships among Feature Model Views"
VM4ModernTech 2022 will be held as a workshop at SPLC 2022.
We are happy to announce that Prof. Dr. Mathieu Acher will give the keynote for our workshop.
Machine learning and deep software variability
At compile-time or at runtime, varying software is a powerful means to achieve optimal functional and performance goals. An observation is that only considering the software layer might be naive to tune the performance of the system or test that the functionality behaves correctly. In fact, many layers (hardware, operating system, input data, build process etc.), themselves subject to variability, can alter performances of software configurations. For instance, configurations' options may have very different effects on execution time or energy consumption when used with different input data, depending on the way it has been compiled and the hardware on which it is executed.
In this talk, I will introduce the concept of “deep software variability” which refers to the interactions of all external layers modifying the behavior or non-functional properties of a software system. I will show how compile-time options, inputs, and software evolution (versions), some dimensions of deep variability, can question the generalization of the variability knowledge of popular configurable systems like Linux, gcc, xz, or x264.
I will then argue that machine learning (ML) is particularly suited to manage very large variants space. The key idea of ML is to build a model based on sample data -- here observations about software variants in variable settings -- in order to make predictions or decisions. I will review state-of-the-art solutions developed in software engineering and software product line engineering while connecting with works in ML (e.g., transfer learning, dimensionality reduction, adversarial learning). Overall, the key challenge is to leverage the right ML pipeline in order to harness all variability layers (and not only the software layer), leading to more efficient systems and variability knowledge that truly generalizes to any usage and context.
From this perspective, we are starting an initiative to collect data, software, reusable artefacts, and body of knowledge related to (deep) software variability: https://deep.variability.io
Finally, I will open a broader discussion on how machine learning and deep software variability relate to the reproducibility, replicability, and robustness of scientific, software-based studies (e.g., in neuroimaging and climate modelling).
Gökhan Kahraman and Loek Cleophas: "A Tool for Modeling and Analysis of Relationships among Feature Model Views"
We will join sessions with the VariVolution Workshop. The schedule can be found on the SPLC 2022 website.
15+5 minutes (presentation + discussion)
Please refer to the SPLC 2022 website.
We encourage submissions that push the state-of-the-art in research and practice of variability management in the following domains (but not limited to):
Automotive software
Autonomous driving
Bots in software engineering
Cloud computing
Cyber-physical systems
Internet of things
(Micro-)service architectures
ML/AI-based systems
Robotics
The important dates (AoE) for the workshop are aligned with the general workshop due dates of SPLC 2022:
Workshop papers submission: June 14, 2022 (updates will be possible until June 16)
Workshop papers notification: July 1, 2022
Final version of papers (camera ready): July 7, 2022
All these times are AoE.
The workshop seeks submissions of different types and degrees of maturity in order to be inclusive of both researchers and practitioners and provide a lively platform for discussion also for early concepts and ideas.
Submissions must follow the ACM Master Article Template: https://www.acm.org/publications/proceedings-template
Latex users are indicated to use the “sigconf” option, so they are recommended to use the template that can be found in “sample-sigconf.tex”. In this way, the following latex code can be placed at the start of the latex document:
\documentclass[sigconf,review]{acmart}
\acmConference[SPLC’22]{26th ACM International Systems and Software Product Lines Conference}{12-16 September, 2022}{Graz, Austria}
Submission link: https://easychair.org/conferences/?conf=splc2022
Papers submissions can be
Regular papers of up to 8 (+2 for references only) pages that present original research or industrial experiences.
Short papers of 4 (+2 for references only) pages that present sound new ideas, preliminary findings, or research visions.
All submission will be peer-reviewed by at least three program committee members. All accepted submissions, regardless of their level of maturity, will be given a presentation slot at the workshop.
The workshop does NOT follow a double-blind review process.
Jacob Krüger (Ruhr-University Bochum, Germany)
Wesley K. G. Assunção (Johannes Kepler University Linz, Austria and Pontifical Catholic University of Rio de Janeiro, Brazil)
Inmaculada Ayala (Universidad de Málaga, Spain)
Sébastien Mosser (McMaster Unviersity, Canada)
Cristina Vicente-Chicote (University of Extremadura, Spain)
Federico Ciccozzi (Mälardalen University in Västerås, Sweden)
Loek Cleophas (Eindhoven University of Technology, The Netherlands & Stellenbosch University, South Africa)
Lidia Fuentes (University of Málaga, Spain)
Thomas Leich (Harz University Wernigerode, Germany)
Rick Rabiser (Johannes Kepler University Linz, Austria)
Sandro Schulze (TU Braunschweig)
Daniel Strüber (Chalmers | University of Gothenburg, Sweden)
For further questions about the workshop, feel free to contact the workshop organizers:
Jacob Krüger, Wesley K. G. Assunção, Inmaculada Ayala, Sébastien Mosser