Software Quality Journal

, Volume 15, Issue 4, pp 383–399 | Cite as

Improving the quality of use case descriptions: empirical assessment of writing guidelines

  • Keith Thomas PhalpEmail author
  • Jonathan Vincent
  • Karl Cox


Use cases are the main requirements vehicle of the UML and are used widely to specify systems. Hence, the need to write clear and accurate use case descriptions has a significant impact for many practitioners. However, many have pointed to weaknesses in the support offered to those writing use cases, and a number of authors advocate the use of rules in the composition and structuring of use case descriptions. These rules constrain the user, by only allowing certain grammatical constructions, typically guiding the structure or the style of the description For example, the CREWS research project pioneered Use Case Authoring Guidelines, suggesting that the adoption of such guidelines improved resulting use case descriptions. Replication of CREWS studies appeared to confirm the view that use case descriptions were improved through the application of guideline sets, but also noted that learning such rules presented a significant overhead. Hence, a leaner set of guidelines (the CP rules) was developed.

This paper describes empirical work to assess the utility of these two sets of writing guidelines (CREWS and CP). In particular, descriptions are assessed against a set of established criteria—a use case quality description checklist, which the authors described in a previous paper.

Our findings suggest that the leaner set of guidelines performs at least as well in terms of their ability to produce clear and accurate (comprehensible) descriptions. Hence, that a tractable set of rules may prove applicable to the industrial context, which could lead to effective validation of use cases.


Software Engineering Case Description Prepositional Phrase Consistent Structure Guideline Usage 
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.


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Keith Thomas Phalp
    • 1
    Email author
  • Jonathan Vincent
    • 1
  • Karl Cox
    • 2
  1. 1.Software Systems Modelling GroupBournemouth UniversityDorsetUK
  2. 2.Empirical Software Engineering GroupNational ICT Australia, Australian Technology Park EveleighSydneyAustralia

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