Improving Naïve Bayes classifier for software architecture reconstruction

Documentation of software architecture is a good approach to understand the architecture of a software system and to match it with the changes needed during the software maintenance phase. In several systems like legacy or older systems these documents are not available or if available; they are not up-to-date and usable. So, reconstruction of architecture in order to maintain these systems is a necessary activity. When we talk about software architecture, usually we are looking for a modular view of architecture with low coupling and high cohesion. In this paper, we try to improve the algorithm of machine-learning which is presented before for architecture recovery, and we propose using it for architecture reconstruction in order to obtain optimum modularity in the architecture with low coupling and high cohesion. The proposed algorithm is evaluated in a case study, and its results are presented.