(Automated) Software Modularization Using Community Detection
- 1.2k Downloads
The modularity of a software system is known to have an effect on, among other, development effort, change impact, and technical debt. Modularizing a specific system and evaluating this modularization is, however, challenging. In this paper, we apply community detection methods to the graph of class dependencies in software systems to find optimal modularizations through communities. We evaluate this approach through a study of 111 Java systems contained in the Qualitas Corpus. We found that using the modularity function of Newman with an Erdős-Rényi null-model and using the community detection algorithm of Reichardt and Bornholdt improved community quality for all systems, that coupling decreased for 99 of the systems, and that coherence increased for 102 of the systems. Furthermore, the modularity function correlates with existing metrics for coupling and coherence.
KeywordsSoftware architecture Module structure Software modularity
Unable to display preview. Download preview PDF.
- 1.Baldwin, C.Y., Clark, K.B.: Design Rules: The Power of Modularity, vol. 1. MIT Press, Cambridge (2000)Google Scholar
- 3.Li, Z., Liang, P., Avgeriou, P., Guelfi, N., Ampatzoglou, A.: An empirical investigation of modularity metrics for indicating architectural technical debt. In: Proceedings of the 10th International ACM Sigsoft Conference on Quality of Software Architectures, QoSA 2014, New York, NY, USA, pp. 119–128. ACM (2014)Google Scholar
- 4.Abdeen, H., Ducasse, S., Sahraoui, H.: Modularization metrics: Assessing package organization in legacy large object-oriented software. In: 2011 18th Working Conference on Reverse Engineering (WCRE), pp. 394–398, October 2011Google Scholar
- 5.Abdeen, H., Sahraoui, H., Shata, O., Anquetil, N., Ducasse, S.: Towards automatically improving package structure while respecting original design decisions. In: 2013 20th Working Conference on Reverse Engineering (WCRE), pp. 212–221. IEEE (2013)Google Scholar
- 8.Martin, R.C.: The tipping point: Stability and instability in OO design. Dr Dobb’s, March 2005Google Scholar
- 13.Gentea, A., Madsen, T.: Using community detection methods for automated software architecture recovery. Master’s thesis, Department of Computer Science, University of Copenhagen, September 2014Google Scholar
- 16.Wohlin, C., Runeson, P., Höst, M., Ohlsson, M.C., Regnell, B., Wesslén, A.: Experimentation in software engineering. Springer (2012)Google Scholar
- 17.Tempero, E., Anslow, C., Dietrich, J., Han, T., Li, J., Lumpe, M., Melton, H., Noble, J.: Qualitas corpus: A curated collection of java code for empirical studies. In: 2010 Asia Pacific Software Engineering Conference (APSEC 2010), pp. 336–345, December 2010Google Scholar
- 18.Bruneton, E., Lenglet, R., Coupaye, T.: ASM: A code manipulation tool to implement adaptable systems. In: Adaptable and Extensible Component Systems, Grenoble, France, November 2002Google Scholar
- 19.Wong, K.: Rigi Users Manual. Department of Computer Science, University of Victoria, July 1996. http://www.rigi.cs.uvic.ca/downloads/rigi/doc/user.html