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Community Detection Algorithms Using Modularity on Function Call Network of Software

Yang Guo, Zhengxu Zhao

Abstract


Networks, also called graphs mathematically, provide a useful representation of the structure of complex networks. We have focused on static analyses of Windows software by virtue of function calling and found them to reveal small-world, scale-free features similar to those identified in other sociological and technological systems. In this paper, we have pursued a community detecting approach to the study of software engineering at the function level. We first establish the Function Call Network of Software using an open source software system and then apply the greedy optimization for detecting the community structure to this network, which is based on the quality function known as ‘‘modularity’’ over the possible divisions of a network. This is first shown by identifying function communities in the Function Call Network of Software with 2290 functions linked by 22435 edges.

Keywords


function-call graph, power-law distribution, modularity, community detection, greedy optimization.

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