An Empirical Study Of The Robustness Of Two Module Clustering Fitness Functions

Two of the attractions of search-based software engineering (SBSE) derive from the nature of the fitness functions used to guide the search. These have proved to be highly robust (for a variety of different search algorithms) and have yielded insight into the nature of the search space itself, shedding light upon the software engineering problem in hand.This paper aims to exploit these two benefits of SBSE in the context of search based module clustering. The paper presents empirical results which compare the robustness of two fitness functions used for software module clustering: one (MQ) used exclusively for module clustering. The other is EVM, a clustering fitness function previously applied to time series and gene expression data.The results show that both metrics are relatively robust in the presence of noise, with EVM being the more robust of the two. The results may also yield some interesting insights into the nature of software graphs.