Software architecture decomposition using adaptive K-nearest neighbor algorithm

Software architecture decomposition plays an important role in software design cascading effect on various development phases. Software designer decomposes software based on his/her experience. Though it may work well for some, in reality many systems failed to meet the requirements as a result of poor design. Software architecture decomposition using clustering techniques has been investigated in software engineering research. This paper presents an enhanced approach for software architecture decomposition. We used two hierarchical agglomerative clustering methods and adaptive K-nearest neighbor algorithm in this enhanced approach and applied it on two industrial software systems. Results show that the approach provides objective and insightful information for software designer.