Abstract
Refactoring is a maintenance task that aims at improving the quality of a software source code by restructuring it without altering its external behavior. Identifying refactoring opportunities by manually inspecting and analyzing the source code of the system under consideration is a time consuming and costly process. Researchers, in this area, typically introduce fully or semi-automated techniques to determine or predict refactoring candidates and they report related evaluation studies. The quality of the performed studies has a great impact on the accuracy of the obtained results. In this paper, we demonstrate an application for a proposed framework that evaluates published primary studies (PSs) on refactoring prediction/identification techniques. The framework is applied on 47 selected PSs to evaluate the quality of the studies based on their design, conduct, analysis, and conclusion. We used the results to comment on the weaknesses of the existing PSs and the issues that have to be considered with more attention in future studies.
Details
| Journal Section | Articles |
|---|---|
| Authors | |
| Publication Date | May 25, 2016 |
| Submission Date | October 29, 2015 |
| Published in Issue | Year 2016 Volume: 9 Issue: 2 |