Static analysis for dynamic coupling measures

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 ...

• Type: Conference
• Year: 2006
• Where: CASCON
• Authors: Yin Liu, Ana Milanova
• Keywords: CASCON 2006, CASCON 2007, Coupling Measures, Dynamic Coupling Measures, Quality Attributes