Detection of Optimal Refactoring Plans For Resolution of Code Smells

Abstract: Bad smells can be detected using various kinds of automated tools. The problem behind this is clear, where the smell being refactored may have dependency in increasing or resolving some other kind of smell which in turn results in increased effort and time. A smell being resolved may affect the presence of an existing smell or introduces some more conflicts into the system. The works discussed in the literature leads to lot of human effort and enormous amount of maintenance time. In order to reduce the manual work load and to obtain the better source code for easy maintenance and to obtain a better refactoring sequence this work proposes optimal refactoring plans that enhances detection and sequencing of bad smells. The selected code smells are sequenced to avoid RIPPLE EFFECT. The refactoring methods that have to be applied to the source code are also ordered based on the fitness criteria using a genetic algorithm.