Software Module Clustering Using Bio-Inspired Algorithms

Nature has always been a source of inspiration for human beings. Large numbers of complex optimization problems have been solved by the techniques inspired by nature. Software modularization is one of such complex problems that have been encountered by software engineers. It is the process of organizing modules of a software system into optimal clusters. In this chapter, some bio-inspired algorithms such as bat, artificial bee colony, black hole and firefly algorithm have been proposed for the cause of software modularization. The hybrid of these algorithms with crossover and mutation operators of the genetic algorithm has also been proposed. All the algorithms along with their hybrids are tested on seven benchmark open source software systems. It has been evaluated from the results thus obtained that the hybrid of these algorithms proved to optimize better than the existing genetic and hill-climbing approaches.

An Integrated Method for Improving Remodularization in Software Systems using Probability Method

Effective software system must advance to stay pertinent, however this procedure of development can cause the product design to rot and prompt essentially diminished efficiency and even dropped projects. Remodularization tasks can be performed to fix the structure of a software system and evacuate the disintegration brought about by programming advancement. Software remodularization comprises in rearranging software entities into modules to such an extent that sets of substances having a place with similar modules are more comparable than those having a place with various modules.However, re-modularizing systems automatically is challenging in order to enhance their sustainability. In this paper, we have introduced a procedure of automatic software remodularization that helps software maintainers to enhance the software modularization quality by assessing the coupling and attachment among programming components. For precision coupling measures, the proposed technology uses structural coupling measurements. The proposed methodology utilizes tallying of class' part capacities utilized by a given class as a basic coupling measure among classes. The interaction between class files measures structural connections between software elements (classes). In this paper, probability based remodularization (PBR) approach has been proposed to remodularize the software systems. The file ordering process is done by performing probability based approach and remodularization is done based on the dependency strength or connectivity among the files. The proposed technique is experimented on seven software systems. The efficiency is measured by utilizing Turbo Modularization Quality (MQ) that promotes edge weighing module dependence graph (MDG). It very well may be presumed that when comparing performance with the subsisting techniques, for instance, Bunch – GA (Genetic Algorithm), DAGC (Development of Genetic Clustering Algorithm) and Estimation of Distribution Algorithm (EDA), the proposed methodology has greater Turbo MQ value and lesser time complexity with Bunch-GA in the software systems assessed