A Visual Analysis Approach to Support Perfective Software Maintenance

Ensuring code quality represents an essential task in perfective software maintenance, which aims at keeping future maintenance costs low by facilitating adaptations of complex software systems. For this purpose, changes and related efforts have to be identified that imply high positive impact on future maintainability. In this paper, we propose a novel assessment method that applies visual analysis techniques to combine multiple indicators for low maintainability, including code complexity and entanglement with other parts of the system, and recent changes applied to the code. The approach generally helps to identify modules that impose a high risk by causing increased future maintenance efforts. Consequently, it allows for exploration, analysis, and planning of different preventive measures that, e.g., most likely will have a high return on investment. In our tool implementation, we use circular bundle views, extended by the third dimension in which indicators can be mapped to. We have evaluated our approach by conducting a case study based on our tool for a large-scale software system of an industry partner.