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The Design of SREE — A Prototype Potential Ambiguity Finder for Requirements Specifications and Lessons Learned

  • Sri Fatimah Tjong
  • Daniel M. Berry
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7830)

Abstract

[Context and Motivation] Many a tool for finding ambiguities in natural language (NL) requirements specifications (RSs) is based on a parser and a parts-of-speech identifier, which are inherently imperfect on real NL text. Therefore, any such tool inherently has less than 100% recall. Consequently, running such a tool on a NL RS for a highly critical system does not eliminate the need for a complete manual search for ambiguity in the RS. [Question/Problem] Can an ambiguity-finding tool (AFT) be built that has 100% recall on the types of ambiguities that are in the AFT’s scope such that a manual search in an RS for ambiguities outside the AFT’s scope is significantly easier than a manual search of the RS for all ambiguities? [Principal Ideas/Results] This paper presents the design of a prototype AFT, SREE (Systemized Requirements Engineering Environment), whose goal is achieving a 100% recall rate for the ambiguities in its scope, even at the cost of a precision rate of less than 100%. The ambiguities that SREE searches for by lexical analysis are the ones whose keyword indicators are found in SREE’s ambiguity-indicator corpus that was constructed based on studies of several industrial strength RSs. SREE was run on two of these industrial strength RSs, and the time to do a completely manual search of these RSs is compared to the time to reject the false positives in SREE’s output plus the time to do a manual search of these RSs for only ambiguities not in SREE’s scope. [Contribution] SREE does not achieve its goals. However, the time comparison shows that the approach to divide ambiguity finding between an AFT with 100% recall for some types of ambiguity and a manual search for only the other types of ambiguity is promising enough to justify more work to improve the implementation of the approach. Some specific improvement suggestions are offered.

Keywords

Manual Search Requirement Engineer Software Requirement Manual Examination Lexical Analyzer 
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-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sri Fatimah Tjong
    • 1
  • Daniel M. Berry
    • 2
  1. 1.University of NottinghamSemenyihMalaysia
  2. 2.Cheriton School of Computer ScienceUniversity of WaterlooWaterlooCanada

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