Call for Data Track Papers

RE'17 introduces a new Data Track: We provide industry-relevant challenges and industrial artifacts, consisting of requirements, models and specifications, to allow researchers to apply their techniques to these artifacts and report their results in a challenge paper. We also encourage submissions that present a data set together with research questions.

RE Data Challenge (4-6 Pages)

For this year's RE data challenge, we present sample data sets and four challenges. The questions that can be answered based on these datasets include the following high level challenges:

  • Automatic trace link discovery
  • Identification of (types of) requirements, e.g. features or quality requirements
  • Extraction of knowledge (e.g. glossary terms, implied data models)
  • Analyze requirements (e.g. extraction of implied goal models, ambiguity analysis)

Submissions should answer research questions related to a specific research problem within the challenge areas defined previously. We ask you to come up with a RE related research problem and present results that improves the state of the art. You can use examples of the datasets provided by us or use and release your own dataset.

Dataset Challenge Area 1 (Tracing) Challenge Area 2 (Identify requirements) Challenge Area 3 (Extract knowledge) Challenge Area 4 (Analyze requirements)
Quality Attributes (NFR)
Direct link
e.g. Trace by classification e.g. Identify Functional and Non-Functional requirements e.g. Extract Goal Models from Req; Assess requirements quality
Direct Link
e.g. Identify quality requirements (here: security) e.g. Extract glossaries, implied data model
Direct link
e.g. Trace high-level requirements to low-level requirements e.g. Build Goal Models from Requirements
How to participate in the challenge

Participating in the challenge requires you to:

  1. Familiarize yourself with one or more of the data sets and its infrastructure.
  2. Access and analyze the data set(s) of your choice.
  3. Report your findings in a four-page document.
  4. Challenge papers are encouraged to be reproducible, the authors are encouraged to release tools, algorithms and share the specific parameters of the experiments promoting reproducibility.
  5. If your report is accepted, present your results at RE!

RE Data Showcase (4 Pages)

In addition to the challenges we also encourage high quality submissions that describe a publicly available data set as well as the research questions or broad research challenges that can be investigated and benchmarked based on this data. Data showcase papers can help define new challenges for the next year.

Data showcase papers are expected to include:

  • A description of the dataset, including the sources from which the data is obtained
  • Link to download the dataset
  • The methodology used to obtain the datasets. It is encouraged to release tools which are used to generate the datasets
  • A brief description of the formatting used to store the dataset
  • Research topics enabled by the dataset and what type of research questions could be answered or what further improvements could be made to the data set, and
  • Any limitations and/or challenges of this data set.

Please submit your data papers in PDF format via EasyChair. Select the RE'17 Data Track for your submission.

Key Dates

Data Available Monday, October 17,
Full Paper Submission Tuesday, April 11
Paper Notification Thursday, May 11
Camera Ready Due Friday, June 23

All deadlines are 23:59 Anywhere on Earth (Standard Time).


Any inquiries regarding data track papers can be directed to the Data Track Co-Chairs:

Data Track Co-Chair

Eric Knauss
Chalmers, University of Gothenburg, Sweden

Data Track Co-Chair

Mehdi Mirakhorli
Rochester Institute of Technology, USA

Data Track PC Members

  • Daniela Damian, University of Victoria
  • Neil Ernst, Software Engineering Institute
  • João M. Fernandes, Universidade do Minho
  • Jin Guo, University of Notre Dame
  • Jennifer Horkoff, Chalmers University of Technology
  • Patrick Mäder, Ilmenau Technical University
  • Thorsten Merten, University of Bonn
  • Pradeep Kumar Murukannaiah, Rochester Institute of Technology
  • Nan Niu University of Cincinnati
  • Mona Rahimi, DePaul University
  • Miroslaw Staron, University of Gothenburg