Reducing software complexity by hidden structure analysis : Methods to improve modularity and decrease ambiguity of a software system

Abstract: Software systems can be represented as directed graphs where components are nodes and dependencies between components are edges. Improvement in system complexity and reduction of interference between development teams can be achieved by applying hidden structure analysis. However, since systems can contain thousands of dependencies, a concrete method for selecting which dependencies that are most beneficial to remove is needed. In this thesis two solutions to this problem are introduced; dominator- and cluster analysis. Dominator analysis examines the cost/gain ratio of detaching individual components from a cyclic group. Cluster analysis finds the most beneficial subgroups to split in a cyclic group. The aim of the methods is to reduce the size of cyclic groups, which are sets of co- dependent components. As a result, the system architecture will be less prone to propagating errors, caused by modifications of components. Both techniques derive from graph theory and data science but have not been applied to the area of hidden structures before. A subsystem at Ericsson is used as a testing environment. Specific dependencies in the structure which might impede the development process have been discovered. The outcome of the thesis is four to-be scenarios of the system, displaying the effect of removing these dependencies. The to-be scenarios show that the architecture can be significantly improved by removing few direct dependencies.