SPLC 2020 : International Systems and Software Product Line Conference

The following list of topics aims to summarize those areas in more detail, whereas we also invite submissions to related topics. If in doubt, feel free to ask the track chairs (see below).

Business process management, economics, and organizational issues of product-line engineering
Development process models for variation (e.g., proactive, reactive, extractive, and agile development)
Domain analysis, requirements engineering, and feature traceability
Variability management and variability modeling (e.g., feature models, decision models)
Architecture, design, and visualization of product lines
Specification and modeling of product lines (e.g., domain-specific modeling, model-driven engineering, model transformations, generative modeling)
Realization and implementation techniques for reuse (e.g., domain-specific languages, modules, components, cloud services, platforms, frameworks, plug-ins, apps, preprocessors, feature toggles, version control systems, variation control systems)
Programming languages, model-driven engineering, and domain-specific languages for variation
Testing of configurable systems (e.g., product sampling, test-case selection and prioritization, model-based testing, coverage, mutations, debugging, automatic repair, A/B testing)
Static quality assurance techniques for product lines (e.g., formal methods, program analysis, model checking, consistency checking, validation, verification)
Modeling, analysis, and optimization of non-functional properties (e.g., performance, energy efficiency, interoperability, adaptability, maintainability, dependability, reuse, scalability, reliability)
Security, safety, and synthesis of product lines
Configuration management and automated deployment
Evolution, maintenance, and continuous integration for product lines (e.g., DevOps)
Reverse engineering, variability mining, and refactoring of variability (e.g., migration from clone-and-own, design patterns)
Multi product lines, software ecosystems, program families, product lines of product lines, systems of systems
Knowledge-based and rule-based configuration
Configurability for cyber-physical systems and applications to big data
(Self-)adaptive systems, reconfigurable systems, and dynamic software product lines
Green and sustainable technologies for variation
Human and social aspects of product lines (e.g., collaborative modeling and development, cooperative configuration processes, program comprehension)
Recommendation systems and artificial intelligence for configurators and feature models (e.g., SAT solvers, BDD, CSP solvers, SMT solvers, answer set programming, explanations)
Genetic algorithms, neural networks, and machine learning for product lines
Education of product lines and configurators (e.g., teaching, training, dissemination)
Methods, tools, and data sets targeting variation
Empirical evaluations of all topics above (user studies, case studies, controlled experiments, surveys, rigorous measurements)