Can Automated Impact Analysis Techniques Help Predict Decaying Modules?

A decaying module refers to a module whose quality is getting worse and is likely to become smelly in the future. The concept has been proposed to mitigate the problem that developers cannot track the progression of code smells and prevent them from occurring. To support developers in proactive refactoring process to prevent code smells, a prediction approach has been proposed to detect modules that are likely to become decaying modules in the next milestone. Our prior study has shown that modules that developers will modify as an estimation of developers' context can be used to improve the performance of the prediction model significantly. Nevertheless, it requires the developer who has perfect knowledge of locations of changes to manually specify such information to the system. To this end, in this study, we explore the use of automated impact analysis techniques to estimate the developers' context. Such techniques will enable developers to improve the performance of the decaying module prediction model without the need of perfect knowledge or manual input to the system. Furthermore, we conduct a study on the relationship between the accuracy of an impact analysis technique and its effect on improving decaying module prediction, as well as the future direction that should be explored.