Introducing a Ripple Effect Measure: A Theoretical and Empirical Validation

Abstract

Context: Change impact analysis investigates the negative consequence of system changes, i.e., the propagation of changes to other parts of the system (also known as the ripple effect). Identifying modules of the system that will be affected by the ripple effect is an important activity, before and after the application of any change. Goal: However, in the literature, there is only a limited set of studies that investigate the probability of a random change occurring in one class, to propagate to another. In this paper we discuss and evaluate the Ripple Effect Measure (in short REM), a metric that can be used to assess the aforementioned probability. Method: To evaluate the capacity of REM as an assessor of the prob-ability of a class to change due to the ripple effect, we: (a) mathematically validate it against established metric properties (e.g., non-negativity, monotonicity, etc.), proposed by Briand et al., and (b) empirically investigate its validity as an assessor of class proneness to the ripple effect, based on the 1061-1998 IEEE Standard on Software Measurement (e.g., correlation, predictive power, etc.). To apply the empirical validation process, we conducted a holistic multiple-case study on java open-source classes. Results: The results of REM validation (both mathematical and empirical) suggest that REM is a theoretically sound measure that is the most valid assessor of the probability of a class to change due to the ripple effect, compared to other existing metrics.