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Improving Near-Duplicate Detection in Multi-Layered Collaborative Requirements Engineering Discussions Through Discussion Clustering

  • Christian Sillaber
  • Ruth Breu
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Existing methods for finding near-duplicate content often fail when applied to informal user discussions spanning multiple messages, which can be found in collaborative requirement discussions. As a result, although the underlying knowledge sharing platform already contains duplicated entries the stakeholders often recreate already existing requirements discussions without contributing to the existing discussions. In this paper we therefore identify common reasons leading to near-duplicate content and develop a new algorithm for detecting near-duplicate content in multilevel requirement discussions. The algorithm is implemented using a large case study of real-world collaborative requirements engineering platforms serving hundreds of thousands of stakeholders. Our preliminary results show, that we outperform existing search algorithms and that we are able to identify near-duplicates in multilevel requirement discussions with high precision.

Keywords

Discussion Thread Previous Message Message Body Requirement Discussion Empty Message 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The research herein is partially conducted within the competence network Softnet Austria (www.soft-net.at) and funded by the Austrian Federal Ministry of Economics (bm:wa), the province of Styria, the Steirische Wirtschaftsförder-ungsgesellschaft mbH. (SFG), and the city of Vienna in terms of the center for innovation and technology (ZIT). This work was supported by the project “QE LaB–Living Models for Open Systems (FFG 822740)” and partially funded by the European Commission under the FP7 project “PoSecCo” (IST 257129).

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of InnsbruckInnsbruckAustria

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