A systematic review of refactoring opportunities by software antipattern detection

• Published in: Automated software engineering Vol. 31; no. 2; p. 42
• Main Authors: Kalhor, Somayeh, Keyvanpour, Mohammad Reza, Salajegheh, Afshin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Automation; Computer engineering; Computer Science; Methods; Qualitative analysis; Software development; Software engineering; Software Engineering/Programming and Operating Systems; Source code; Systematic review; Software bad smell; Defect; Code smell; Antipattern detection; Refactoring opportunities
• ISSN: 0928-8910; 1573-7535
• DOI: 10.1007/s10515-024-00443-y

The violation of the semantic and structural software principles, such as low connection, high coherence, high understanding, and others, are called anti-patterns, which is one of the concerns of the software development process. They are caused due to bad design or programming that must be detected and removed to improve the application’s source code. Refactoring operators efficiently eliminate antipatterns, but they must first be identified. Therefore, antipattern detection is a critical issue in software engineering, and to do this, various approaches have been proposed. So far, review articles have been published to classify and compare these approaches. However, a comprehensive study using evaluation parameters has not compared different anti-pattern detection methods at all software abstraction levels. In this article, all the methods presented so far are classified, then their advantages and disadvantages are highlighted. Finally, a complete comparison of each category by evaluation metrics is provided. Our proposed classification considers three aspects, levels of abstraction, degree of dependence on developers’ skills, and techniques used. Then, the evaluation metrics reported on this subject are analyzed, and the qualitative values of these metrics for each category are presented. This information can help researchers compare and understand existing methods and improve them.