Editorial for the special issue on software refactoring: Application breadth and technical depth

Abstract

The field of software refactoring has been evolving rapidly, driven by the need to maintain software quality and manage complexity as software systems grow. This special issue of the Journal of Software: Evolution and Process focuses on “Software Refactoring: Application Breadth and Technical Depth,” aiming to promote research that advances our understanding of software refactoring practices and their impact. The selected papers in this issue reflect the broad scope and technical depth of the topic, covering a variety of innovative methodologies, empirical studies, and new tools that push the boundaries of current software refactoring research.

The paper “Software Refactoring Side Effects” by AbuHassan, Alshayeb, and Ghouti addresses the often-overlooked side effects of software refactoring. The authors propose three different approaches for handling these side effects and employ a multi-objective optimization algorithm to identify optimal refactoring decisions. Their work enhances the understanding of refactoring's unintended consequences and proposes practical solutions, fitting well into the theme of exploring new dimensions of refactoring practices.

Zhao et al., in their article “An Architecture Refactoring Approach to Reducing Software Hierarchy Complexity,” introduce a novel metric to quantify software hierarchy complexity and propose the Complexity-oriented Software Architecture Refactoring (CoSSR) approach. Their study demonstrates how reducing software hierarchy complexity can improve software quality, aligning with the issue's focus on reducing complexity through innovative refactoring techniques.

The article “MARS: Detecting Brain Class/Method Code Smell Based on Metric-Attention Mechanism and Residual Network” by Zhang and Dong presents a new method for detecting complex code smells using a novel deep learning approach. By leveraging a metric-attention mechanism and an improved residual network, their model shows superior performance compared to existing methods. This research contributes to the special issue by applying cutting-edge machine learning techniques to refactoring challenges.

AlOmar et al.'s study, “Behind the Scenes: On the Relationship Between Developer Experience and Refactoring,” investigates the correlation between developer experience and refactoring activities across 800 open-source projects. Their findings highlight that experienced developers tend to perform more varied refactoring operations but document less of their refactoring activities. This study offers a new perspective on how human factors influence refactoring practices, enriching the broader conversation on effective software evolution.

In “A Study of Refactorings During Software Change Tasks,” Eilertsen and Murphy explore the reasons behind the disuse of refactoring tools during software change tasks. Their observational study reveals that the strategies developers use significantly impact the use of refactoring tools, pointing to a gap between available tool support and actual developer needs. This paper provides valuable insights into the practical challenges of implementing refactoring in real-world settings.

Halepmollasi and Tosun, in “Exploring the Relationship Between Refactoring and Code Debt Indicators,” present an empirical analysis of the association between different types of refactorings, code smells, and faults. Their findings challenge previous assumptions about refactoring's impact on code smells and suggest that refactoring activities might be more fault-inducing than previously thought. This research adds a critical dimension to understanding the effects of refactoring on software quality.

Lastly, Abdou and Darwish's article, “Severity Classification of Software Code Smells using Machine Learning Techniques: A Comparative Study,” evaluates various machine learning models for classifying the severity of code smells. Their use of the LIME algorithm for model interpretability provides new insights into predicting code smell severity, contributing to the issue's exploration of data-driven approaches to software maintenance.

Recent developments in generative AI and machine learning have opened new avenues for advancing software refactoring practices. As demonstrated by some of the studies in this issue, these technologies can automate and enhance refactoring processes, improve tool support, and provide more accurate code smell detection. The application of AI in this domain is still in its early stages, but it holds great promise for transforming how software systems are maintained and evolved. We encourage researchers to explore these emerging trends further, focusing on integrating AI-driven approaches with traditional software engineering practices.

We extend our sincere appreciation to all the authors for their contributions, to the peer reviewers for their invaluable feedback and dedication, and to the editorial board members for their support in assembling this special issue. Together, these efforts have created a diverse collection of research that we believe will significantly impact the field of software refactoring.