Abstract:
Software evolution continues throughout the life cycle of the software. During the evolution of
software system, it has been observed that the developers have a tendency to copy the modules completely or
partially and modify them. This practice gives rise to identical or very similar code fragments called software
clones. This paper examines the evolution of clone components by using advanced time series analysis.
In the first phase, software clone components are extracted from the source repository of the software
application by using the abstract syntax tree approach. Then, the evolution of software clone components is
analyzed. In this paper, three models, Autoregressive Integrated Moving Average, back propagation neural
network, and multi-objective genetic algorithm-based neural network, have been compared for the prediction
of the evolution of software clone components. Evaluation is performed on the large open-source software
application, ArgoUML. The ability to predict the clones helps the software developer to reduce the effort
during software maintenance activities.
