Predicting change impact from logical models

Abstract

To improve the ability of predicting the impact scope of a given change, we present two approaches applicable to the maintenance of object-oriented software systems. Our first approach exclusively uses a logical model extracted from UML relations among classes, and our other, hybrid approach additionally considers information mined from version histories. Using the open source Hadoop system, we evaluate our approaches by comparing our impact predictions with predictions generated using existing data mining techniques, and with actual change sets obtained from bug reports. We show that both our approaches produce better predictions when the system is immature and the version history is not well-established, and our hybrid approach produces comparable results with data mining as the system evolves.