Refactoring Opportunity Identification Methodology for Removing Long Method Smells and Improving Code Analyzability


IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.7   pp.1766-1779
Publication Date: 2018/07/01
Publicized: 2018/04/26
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017KBP0026
Type of Manuscript: Special Section PAPER (Special Section on Knowledge-Based Software Engineering)
code analyzability,  long method bad smell,  refactoring,  software engineering,  software maintenance,  

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An important step for improving software analyzability is applying refactorings during the maintenance phase to remove bad smells, especially the long method bad smell. Long method bad smell occurs most frequently and is a root cause of other bad smells. However, no research has proposed an approach to repeating refactoring identification, suggestion, and application until all long method bad smells have been removed completely without reducing software analyzability. This paper proposes an effective approach to identifying refactoring opportunities and suggesting an effective refactoring set for complete removal of long method bad smell without reducing code analyzability. This approach, called the long method remover or LMR, uses refactoring enabling conditions based on program analysis and code metrics to identify four refactoring techniques and uses a technique embedded in JDeodorant to identify extract method. For effective refactoring set suggestion, LMR uses two criteria: code analyzability level and the number of statements impacted by the refactorings. LMR also uses side effect analysis to ensure behavior preservation. To evaluate LMR, we apply it to the core package of a real world java application. Our evaluation criteria are 1) the preservation of code functionality, 2) the removal rate of long method characteristics, and 3) the improvement on analyzability. The result showed that the methods that apply suggested refactoring sets can completely remove long method bad smell, still have behavior preservation, and have not decreased analyzability. It is concluded that LMR meets the objectives in almost all classes. We also discussed the issues we found during evaluation as lesson learned.