Predicting Refactoring Activities via Time Series

Refactoring activities should be carefully planned in advance for different reasons: i) they can be expensive in terms of resources needed; ii) refactoring is highly requested when clones, code smells or other potential problems reach a given threshold; and iii) such activities should be performed when the evolution of the software system is in a stable period. This work proposes to apply time series forecasting for determining the future releases in correspondence of which refactoring activities should be performed. The idea is to analyze different metrics (size and complexity, cloning ratio, number of smells, etc.) from past releases to predict the optimal refactoring point.