Engineering requirements with desiree: An empirical evaluation

Feng Lin Li, Jennifer Horkoff, Lin Liu, Alex Borgida, Giancarlo Guizzardi, John Mylopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


The requirements elicited from stakeholders suffer from various afflictions, including informality, vagueness, incompleteness, ambiguity, inconsistencies, and more. It is the task of the requirements engineering process to derive from these a formal specification that truly captures stakeholder needs. The Desiree requirements engineering framework supports a rich collection of refinement operators through which an engineer can iteratively transform stakeholder requirements into a specification. The framework includes an ontology, a formal representation for requirements, as well as a tool and a systematic process for conducting requirements engineering. This paper reports the results of a series of empirical studies intended to evaluate the effectiveness of Desiree. The studies consist of three controlled experiments, where students were invited to conduct requirements analysis using textbook techniques or our framework. The results of the experiments offer strong evidence that with sufficient training, our framework indeed helps users conduct more effective requirements analysis.

Original languageEnglish (US)
Title of host publicationAdvanced Information Systems Engineering - 28th International Conference, CAiSE 2016, Proceedings
EditorsSelmin Nurcan, Pnina Soffer, Marko Bajec, Johann Eder
PublisherSpringer Verlag
Number of pages18
ISBN (Print)9783319396958
StatePublished - 2016
Event28th International Conference on Advanced Information Systems Engineering, CAiSE 2016 - Ljubljana, Slovenia
Duration: Jun 13 2016Jun 17 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other28th International Conference on Advanced Information Systems Engineering, CAiSE 2016

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


  • Controlled experiment
  • Effect size
  • Hypothesis testing
  • Requirements problem


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