Tools, Data, and Demonstrations

The Tools, Data, and Demonstrations track provides an opportunity for live demonstrations of product-line tools, of substantial datasets to be shared with the community, and of practices tackling current industrial challenges.

We invite proposals for demonstrations of academic, open source, in-house, and commercial tools that support and automate any aspect of product-line engineering. Areas of interest include, but are not limited to, feature modeling, variant management, validation and verification, derivation and generation of products, product-line testing, product-line analysis, and measurement and optimization of non-functional properties of product lines. Demonstrations of original, novel tools, of existing tools with new contributions, and of customized extensions of standard tools are welcome. We invite demonstrations of both early implementations and mature tools. Commercial tool vendors are encouraged to demonstrate their tools together with an industrial customer using concrete examples. Each demonstration should explain how the tool could be used to solve real-life problems with a use case.

The availability of datasets is critical for the product-line community in order to develop and evaluate realistic and scalable techniques. To foster sharing such datasets, we invite data papers related to product lines. Data papers should describe a dataset, including:

  • the content and origin of it;
  • the methodology and tools used to obtain it;
  • the schema, structure, or layout to store it;
  • usage scenarios or open analysis challenges of it;
  • relationships to other existing datasets;
  • limitations, omissions, or gaps of the dataset.

The data must be made available to the reviewers upon submission and finally published upon acceptance.

Finally, in addition to tool and data papers, we are interested in proposals for demonstrations of industrial product-line practice. Of special interest are industrial practices that are currently not sufficiently supported by tools.


Papers describing the tool, dataset, or practice should be submitted electronically as PDF files through the “Demonstrations and Tools track” using EasyChair at

Submissions must not exceed 4 pages in the ACM SIG Proceedings format including all text, references, and figures, and must have an appendix of at most 2 pages providing a brief description of how the presentation will be conducted.


Paper Submission: May 16, 2016 (extended to May 23,2016)

Notification of Acceptance: June 6, 2016

Camera-ready Paper Due: June 27, 2016

Conference: Sept. 19-23, 2016


Submissions will be evaluated by at least two PC members according to the relevance and originality of the work as well as presentation quality. Data papers should describe the dataset according to the points outlined above. Work that has not been previously presented at SPLC is given a priority. Tools previously presented at SPLC should include a description of new features of the tool or new aspects to be presented.


Accepted proposals will appear in the SPLC 2016 Proceedings (second volume) published by ACM. At least one author of each accepted submission must register and attend SPLC 2016 in order for the submission to be published. For each accepted submission a formal presentation will be scheduled in the conference program.

For further information, please contact Norbert Siegmund (siegmunn(at) or Thorsten Berger (thorsten.berger(at)



  • Thorsten Berger, Chalmers University of Technology, Sweden
  • Norbert Siegmund, University of Passau, Germany


  • Alexander Grebhahn, University of Passau, Germany
  • Jimmy Liang, University of Waterloo, Canada
  • Max Lillack, University of Leipzig, Germany
  • Leonardo Passos, University of Waterloo, Canada
  • Daniela Rabiser, Johannes Kepler University Linz, Austria
  • Sandro Schulze, Hamburg University of Technology, Germany
  • Christoph Seidl, Technical University Braunschweig, Germany
  • Stefan Stanciulescu, IT University of Copenhagen, Denmark
  • Pavel Valov, University of Waterloo, Canada