This paper presents ongoing work on using data mining clustering to support the evaluation of software systems' maintainability. As input for our analysis we employ software measurement data extracted from Java source code. We propose a two-steps clustering process which facilitates the assessment of a system's maintainability at first, and subsequently an in-cluster analysis in order to study the evolution of each cluster as the system's versions pass by. The process is evaluated on Apache Geronimo, a J2EE 1.4 open source Application Server. The evaluation involves analyzing several versions of this software system in order to assess its evolution and maintainability over time. The paper concludes with directions for future work. © 2009.
Antonellis, P., Antoniou, D., Kanellopoulos, Y., Makris, C., Theodoridis, E., Tjortjis, C., & Tsirakis, N. (2009). Clustering for Monitoring Software Systems Maintainability Evolution. Electronic Notes in Theoretical Computer Science, 233(C), 43–57. https://doi.org/10.1016/j.entcs.2009.02.060