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Towards automated requirements prioritization and triage

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Abstract

Time-to-market deadlines and budgetary restrictions require stakeholders to carefully prioritize requirements and determine which ones to implement in a given product release. Unfortunately, existing prioritization techniques do not provide sufficient automation for large projects with hundreds of stakeholders and thousands of potentially conflicting requests and requirements. This paper therefore describes a new approach for automating a significant part of the prioritization process. The proposed method utilizes data-mining and machine learning techniques to prioritize requirements according to stakeholders’ interests, business goals, and cross-cutting concerns such as security or performance requirements. The effectiveness of the approach is illustrated and evaluated through two case studies based on the requirements of the Ice Breaker System, and also on a set of stakeholders’ raw feature requests mined from the discussion forum of an open source product named SugarCRM.

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Authors and Affiliations

  1. School of Computing, DePaul University, 243 S. Wabash, Chicago, IL, 60604, USA

    Chuan Duan, Paula Laurent, Jane Cleland-Huang & Charles Kwiatkowski

Authors
  1. Chuan Duan
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  2. Paula Laurent
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  3. Jane Cleland-Huang
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  4. Charles Kwiatkowski
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Corresponding author

Correspondence to Jane Cleland-Huang.

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Duan, C., Laurent, P., Cleland-Huang, J. et al. Towards automated requirements prioritization and triage. Requirements Eng 14, 73–89 (2009). https://doi.org/10.1007/s00766-009-0079-7

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