Static analysis for dynamic coupling measures

  • Authors:

  • Yin Liu;Ana Milanova

  • Affiliations:

  • Rensselaer Polytechnic Institute, Troy, NY;Rensselaer Polytechnic Institute, Troy, NY

  • Venue:
  • CASCON '06 Proceedings of the 2006 conference of the Center for Advanced Studies on Collaborative research
  • Year:
  • 2006

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Abstract

Coupling measures have important applications in software development and maintenance. They are used to reason about the structural complexity of software and have been shown to predict quality attributes such as fault-proneness, ripple effects of changes and changeability. Traditional object-oriented coupling measures do not account for polymorphic interactions, and thus underestimate the complexity of classes and fail to properly predict their quality attributes.To address this problem Arisholm et al. [3] define a family of dynamic coupling measures that account for polymorphism. They collect dynamic coupling measures through dynamic analysis and show that these measures are better indicators of complexity and better predictors of quality attributes than traditional coupling measures.This paper presents a new approach to the computation of dynamic coupling measures. Our approach uses static analysis, in particular class analysis, and is designed to work on incomplete programs. We perform experiments on several Java components and present a precision evaluation which shows that inexpensive class analysis such as RTA computes dynamic coupling measures with almost perfect precision. Our results indicate that inexpensive static analysis may be used as a more convenient, more practical and more precise alternative to dynamic analysis for the purposes of computation of dynamic coupling measures.