An empirical study of static program slice size
ACM Transactions on Software Engineering and Methodology (TOSEM)
Empirical study of optimization techniques for massive slicing
ACM Transactions on Programming Languages and Systems (TOPLAS)
Locating dependence structures using search-based slicing
Information and Software Technology
An empirical study of the relationship between the concepts expressed in source code and dependence
Journal of Systems and Software
Combining preprocessor slicing with C/C++ language slicing
Science of Computer Programming
Dependence clusters in source code
ACM Transactions on Programming Languages and Systems (TOPLAS)
Assessing the impact of global variables on program dependence and dependence clusters
Journal of Systems and Software
Proceedings of the 9th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
Dependence cluster visualization
Proceedings of the 5th international symposium on Software visualization
A practice-driven systematic review of dependency analysis solutions
Empirical Software Engineering
Efficient Identification of Linchpin Vertices in Dependence Clusters
ACM Transactions on Programming Languages and Systems (TOPLAS)
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A dependence cluster is a set of program statements all of which are mutually inter-dependent. Such clusters can cause problems for maintenance, because a change to any statement in the cluster will have a potential impact on all statements in the cluster. This paper introduces the concept of dependence clusters and dependence pollution and shows how a simple visualisation can be used to quickly and effectively locate them. The paper presents the results of two empirical studies and several case studies which evaluate the approach. The results indicate the importance of dependence cluster analysis: for a set of 20 programs, ranging in size from 1,170 LoC to 179,623 LoC, 99.6% of clusters identified were within 1% tolerance of being identical, while dependence clusters were found to be surprisingly common: 80% of the programs studied contained clusters of 10% or more of the program.