Semantic vs. syntactic compositions in aspect-oriented requirements engineering: an empirical study

  • Authors:

  • Ruzanna Chitchyan;Phil Greenwood;Americo Sampaio;Awais Rashid;Alessandro Garcia;Lyrene Fernandes da Silva

  • Affiliations:

  • Lancaster University, Lancaster, Gt Britain;Lancaster University, Lancaster, Gt Britain;Lancaster University, Lancaster, Gt Britain;Lancaster University, Lancaster, Gt Britain;Lancaster University, Lancaster, Gt Britain;State University of Rio Grande do Norte, Natal, Brazil

  • Venue:
  • Proceedings of the 8th ACM international conference on Aspect-oriented software development
  • Year:
  • 2009

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Abstract

Most current aspect composition mechanisms rely on syntactic references to the base modules or wildcard mechanisms quantifying over such syntactic references in pointcut expressions. This leads to the well-known problem of pointcut fragility. Semantics-based composition mechanisms aim to alleviate such fragility by focusing on the meaning and intention of the composition hence avoiding strong syntactic dependencies on the base modules. However, to date, there are no empirical studies validating whether semantics based composition mechanisms are indeed more expressive and less fragile compared to their syntax-based counterparts. In this paper we present a first study comparing semantics- and syntax-based composition mechanisms in aspect-oriented requirements engineering (AORE). In our empirical study the semantics-based compositions examined were found to be indeed more expressive and less fragile. The semantics-based compositions in the study also required one to reason about composition interdependencies early on hence potentially reducing the overhead of revisions arising from later trade-off analysis and stakeholder negotiations. However, this added to the overhead of specifying the compositions themselves. Furthermore, since the semantics-based compositions considered in the study were based on natural language analysis, they required initial effort investment into lexicon building as well as strongly depended on advanced tool support to expose the natural language semantics.