Modeling clones evolution through time series

Abstract

The actual effort to evolve and maintain a software system is likely to vary depending on the amount of clones (i.e., duplicated or slightly different code fragments) present in the system. This paper presents a method for monitoring and predicting clones evolution across subsequent versions of a software system. Clones are firstly identified using a metric-based approach, then they are modeled in terms of time series identifying a predictive model. The proposed method has been validated with an experimental activity performed on 27 subsequent versions of mSQL, a medium-size software system written in C. The time span period of the analyzed mSQL releases covers four years, from May 1995 (mSQL 1.0.6) to May 1999 (mSQL 2. 0. 10). For any given software release, the identified models was able to predict the clone percentage of the subsequent release with an average error below 4 %. A higher prediction error was observed only in correspondence of major system redesign.