Abstract: Dependence cluster is a maximal set of program components that all depend upon each other.The current view is that large dependence cluster is extremely universal,which widely exists in all kinds of program source code.The existence of large dependence cluster may lead to the significant ripple effect,i.e.,a change to a certain point of the cluster will cause potential adverse effects on the rest of the cluster,which might affect the whole system.It has a negative impact on many different software engineering activities,including comprehension,testing and maintenance.Dependence cluster detection is a prerequisite for solving adverse effects caused by large dependence cluster.Previous researchers have proposed monotonic slice-size graph (MSG) based method to detect the same slice size of dependence cluster.However,the proposed method is of conservative approximation,which will cause false positives and false negatives.This paper proposed a technique to detect dependence clusters using formal concept analysis (FCA),in which concept inclusiveness is defined to select large concepts.Furthermore,a light-weight computing strategy for FCA was proposed to compute large concept to decrease the computation cost significantly.Based on 12 open source subjects,the empirical comparison showes that the proposed technique can increase the detection accuracy of large dependence clusters with higher efficiency.

Key words: Formal concept analysis,Concept lattice,Dependence cluster