The 3rd International Workshop on Crowd-Based Requirements Engineering (CrowdRE'19)

24 September 2019
Jeju Island, South Korea
Co-located with RE 2019


Submission site:

Submission deadline: 5 9 July 2019 (Extended) AoE time, strict!

We welcome original submissions from research and practice in the following categories:

  • Technical solution papers providing research results with an early validation, which may include tool showcases
    (4–6 pages + 1 page for references)
  • Experience reports that give insights in existing RE practice and potential for application in settings that involve a crowd
    (4–6 pages + 1 page for references)
  • Problem statements explaining industry problems in settings with a large group of stakeholders (2–3 pages)
  • Vision statements explaining strongly explorative ideas, especially towards technology transfer into practice (2–3 pages)

Submissions must describe original works that have not been previously published, are not currently submitted elsewhere, and address at least one of the workshop topics listed below.

Submissions must be written in English and formatted according to the IEEE formatting instructions. All accepted papers will be published in the joint RE 2019 workshop proceedings.

At least one author of every accepted manuscript is expected to attend the entire workshop and present their research.

Important Dates
  • Paper Submission: 9 July 2019 (Extended!)
  • Notification: 25 July 2019
  • Camera Ready: 8 August 2019
  • Workshop: 24 September 2019 (9 AM - 5:30 PM)

All deadlines at 23:59:59 AoE.

Workshop Topics

The following themes of interest for paper submission include, but are not limited to, the following topics. However, each paper should address at least one of these topics:

  • Crowd-based Requirements Engineering (CrowdRE)
  • Analysis of user feedback for RE using Big Data
  • Natural language processing, Information Retrieval, (supervised and unsupervised) Machine Learning, ontologies
  • Crowd-based monitoring and usage mining approaches
  • Case studies and Use Cases involving CrowdRE
  • The contribution of CrowdRE to prioritization, software adaptation, testing and other software engineering aspects
  • The intersection of RE and domains such as sociology, psychology, human factors, and anthropology
  • Approaches to motivate, steer, and boost creativity in the crowd and understand, typify, diversify and engage a crowd for RE
  • Automated RE and the role of the requirements engineer
  • Automated RE and data (safeguarding rollback, privacy, traceability and data integrity; measuring validity, reliability, source quality; processing of rejected data)
  • Platforms and tools supporting CrowdRE

Key Questions and Themes of Interests

Submitted papers should ideally provide contributions relevant to answering one or more of the following key questions that CrowdRE will mainly focus on:

  • What are the achievements and contributions of CrowdRE approaches thus far? How do they contribute to improving RE?
  • What are the risks of going beyond the borders of the 'brown field' domain of RE? To what extent are these risks acceptable? What can be done to mitigate these risks?
  • How can CrowdRE be applied in industry settings? In which parts of the software development lifecycle can CrowdRE play a vital role? Which parts are less suited, and why?
  • What are the central application domains for a CrowdRE approach? What are typical Use Cases in which CrowdRE is applied?
  • How can a holistic solution be provided for a practical application of CrowdRE?
  • How can data from a large group of stakeholders be obtained and interpreted? How can ambiguity and subjectivity be mitigated?
  • How can the reliability of individual crowd members and of the data in general be determined?
  • In what way can crowd members be motivated to contribute the user feedback we require of them?
  • Assuming that the stakeholders form a crowd, how are requirements best elicited, documented, validated, negotiated and managed? How are data from the crowd best obtained and interpreted?
  • In what way could techniques from Big Data analytics be leveraged to analyze heterogeneous and large datasets as a new source for new/changed requirements?
  • Where do the opportunities to collaborate lie? To what extent can the various fields of work be integrated, and where will approaches remain different?