Call for Papers

Data gathering and preparation takes significant amount of time for any software engineering (SE) research including requirements engineering (RE). We aim to facilitate research and contributions on algorithms, models, tools, and techniques by providing a dataset from real world. The International Requirements Engineering Conference has had a mining challenge since last year. In the 2018 edition of the RE conference, we invite papers to participate in a “data challenge” or to provide a “data showcase”.

RE Data Challenge (up to 6 pages)

We offer one challenge problem: Address an RE problem using a data-driven approach (perhaps augmented with some human-in-the-loop machine learning or search-based SE optimization). Each submission MUST have a section “Relevance to RE” where you clearly make the case that your analysis is relevant to some real-world RE problem or a problem addressed in the literature.
To address that challenge, you can use examples of the datasets provided by us or use and release your own dataset. If you use your own datasets, that data must be released to along with your submission. We strongly encourage you to include a reproduction package, hosted on (and registered at, using the instructions from so that other researchers can, in the future, replicate and reuse your results.

Dataset Links
i* models for Goal-oriented requirements engineering

iStar 1 smaller model
iStar 2 larger model
Note that some of these models have been converted to a simple Python format. For details, see and

Web Architectures for Services Platforms WASP
Usecasedocs for model-driven requirements engineering Usecasedocs
Product line See the “feature model repository” at written in the format of
For examples of how these models might be analyzed, see section 5 of HYPERLINK

    • First, papers that are proposing a new problem or solution.
    • Second, papers positioned as the reuse and enhancement of previously published models and methods.

Papers will be evaluated according to their relevance (the problem is worth exploring), originality, scholarship (appropriate consideration of relevant literature), and degree of insight gained (in terms of decision support).
Authors are allowed to use external but openly accessible datasets to define and answer research questions. Such datasets could have been published along with former studies or could be part of the authors’ resources. In the latter case, authors must provide the data permanently on a data repository, preferably on

RE Data Showcase (up to 4 pages)

In addition to the challenges, we also encourage high-quality submissions that describe a publicly available dataset 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. Authors of accepted RE’18 papers are encouraged to write a showcase paper summarizing the resources described in their paper.
Other authors should write up to 4 pages describing their data or challenge problems.
Data showcase papers are expected to include the following (and authors of RE’18 papers can refer to specific sections in their accepted RE’18 paper):

    • A description of the dataset, including the sources from which the data is obtained.
    • Link to download the dataset, from
    • The methodology used to obtain the datasets. It is encouraged to release the tools 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 dataset, and
    • Any limitations and/or challenges of this dataset.

Submission Link

Please submit your data papers in PDF format via EasyChair. Select the RE'18 Data Track for your submission.

Data Track Co-Chairs

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

Tim Menzies

Tim Menzies

Data Co-Chair

NC State University, USA

Maleknaz Nayebi

Maleknaz Nayebi

Data Co-Chair

University of Calgary, Canada