Change Prediction in Object-Oriented Software Systems: A Probabilistic Approach

Abstract

An estimation of change-proneness of parts of a software system is an active topic in the area of software engineering. Such estimates can be used to predict changes to different classes of a system from one release to the next. They can also be used to estimate and possibly reduce the effort required during the development and maintenance phase by balancing the amount of developers’ time assigned to each part of a software system. This research work proposes a novel approach to predict changes in an object-oriented software system. The rationale behind this approach is that in a well-designed software system, feature enhancement or corrective maintenance should affect a limited amount of existing code. Our goal is to quantify this aspect of quality by assessing the probability that each class will change in a future generation. Our proposed probabilistic approach uses the dependencies obtained from the UML diagrams, as well as other code metrics extracted from source code of several releases of a software system using reverse engineering techniques. These measures, combined with the change log of the software system and the expected time of next release, are used in an automated manner to predict whether a class will change in the next release of the software system. The proposed systematic approach has been evaluated on a multiversion medium sized open source project namely JFlex, the Fast Scanner Generator for Java. The obtained results indicate the simplicity and accuracy of our approach in the comparison with existing methods referred in the literature.