Conference Information
SPLC 2020: International Systems and Software Product Line Conference
Submission Date:
2020-05-07 Extended
Notification Date:
Conference Date:
Montreal, Quebec, Canada
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Conference Location
Call For Papers

The International Systems and Software Product Line Conference (SPLC) is the leading conference on variability and configuration of hardware and software. Researchers, practitioners, and educators present and discuss most recent ideas, trends, experiences, challenges, and solutions to advance the state-of-the-art. SPLC is ranked as a top conference, and in its 24th edition, we aim to continue the success of previous years by inviting high quality submissions for the research track.

Even though SPLC started more than two decades ago with a focus on software product lines, it has become an event for numerous closely related areas, such as configurable systems, product configuration, and software variability. The need for extensive exchange between those areas can be illustrated by the following example: There is a fast growing market for 3D printers. The market demands for advances in hardware and software so frequently that efficient configuration techniques are required for both. At the same time, all stakeholders, including managers, marketers, makers, programmers, and customers, want to explore the configuration space and understand the consequences of their decisions. In the context of mass customization, an interdisciplinary research effort is required that unifies researchers from numerous areas.

As started last year, there will be two best paper awards: the Best Research Paper Award and the Best Student Research Paper Award. Each accepted full paper of the research track is considered for the awards, whereas the latter award is devoted to papers with a PhD student as main author.


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)
Last updated by Dou Sun in 2020-04-04
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