Identifying extract class refactoring opportunities for internetware

Abstract

The quality of internetware software is significantly associated with class structure. As software evolves, changes often introduce many unrelated responsibilities to the same classes or distribute tightly-related methods in different classes. These changes make the classes difficult to understand and maintain. Extract class refactoring is an effective technique to improve the quality of software structure by decomposing unrelated methods in one class to create new classes or extracting tightly-related methods from different classes. In this paper, we propose a novel approach for class extraction from internetware source codes. This approach leverages a community structure detection technique to partition software into clusters and extracts classes from the resulting clusters. Our experimental results, which investigate the public well-known internetware PKUAS, indicate that: (1) the proposed approach is much faster than existing search-based clustering approaches (Hillclimbing and Genetic algorithm) and is thus applicable for large-scale internetware; (2) the proposed approach can identify meaningful class extractions for internetware; and (3) Extract Class refactoring candidates identified by the proposed approach significantly improve class cohesion of internetware.