Refactoring Inspection Support for Manual Refactoring Edits

Alves, Everton L. G.; Song, Myoungkyu; Massoni, Tiago; Machado, Patrícia D. L.; Kim, Miryung

doi:10.1109/tse.2017.2679742

Cited by 18 publications

(15 citation statements)

References 40 publications

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“…A considerable effort is spent by the research community in studying traditional and modern code review practices and challenges. This literature has been includes case studies (e.g., [4], [16]), user studies (e.g., [17]), and surveys (e.g., [3], [18]). However, most of the above studies focus on studying the effectiveness of modern code review in general, as opposed to our work that focuses on understanding developers' perception of code review involving refactoring.…”

Section: B Refactoring Awareness and Code Reviewmentioning

confidence: 99%

“…The tool was intended to support developers' review process by distinguishing between refactoring and non-refactoring changes, but it does not provide any insights on the quality of the performed refactoring. Inspired by the work of [16], Alves et al [17] proposed a static analysis tool, called RefDistiller, that helps developers inspect manual refactoring edits. The tool compares two program versions to detect refactoring anomalies' type and location.…”

Section: B Refactoring Awareness and Code Reviewmentioning

confidence: 99%

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Refactoring Practices in the Context of Modern Code Review: An Industrial Case Study at Xerox

AlOmar

Alrubaye

Mkaouer

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

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Modern code review is a common and essential practice employed in both industrial and open-source projects to improve software quality, share knowledge, and ensure conformance with coding standards. During code review, developers may inspect and discuss various changes including refactoring activities before merging code changes in the code base. To date, code review has been extensively studied to explore its general challenges, best practices and outcomes, and socio-technical aspects. However, little is known about how refactoring activities are being reviewed, perceived, and practiced.This study aims to reveal insights into how reviewers develop a decision about accepting or rejecting a submitted refactoring request, and what makes such review challenging. We present an industrial case study with 24 professional developers at Xerox. Particularly, we study the motivations, documentation practices, challenges, verification, and implications of refactoring activities during code review.Our study delivers several important findings. Our results report the lack of a proper procedure to follow by developers when documenting their refactorings for review. Our survey with reviewers has also revealed several difficulties related to understanding the refactoring intent and implications on the functional and non-functional aspects of the software. In light of our findings, we recommended a procedure to properly document refactoring activities, as part of our survey feedback.

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Section: B Refactoring Awareness and Code Reviewmentioning

confidence: 99%

Section: B Refactoring Awareness and Code Reviewmentioning

confidence: 99%

Refactoring Practices in the Context of Modern Code Review: An Industrial Case Study at Xerox

AlOmar

Alrubaye

Mkaouer

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

View full text Add to dashboard Cite

show abstract

“…The Eclipse IDE can be downloaded from the official website at [68]. Eclipse is one of the popular IDEs that supports automated refactoring and is a widely used refactoring tool [69]. Eclipse supports a number of refactoring techniques listed in Fowler's catalog [10].…”

Section: A: Eclipse Refactoring Toolmentioning

confidence: 99%

A Refactoring Classification Framework for Efficient Software Maintenance

et al. 2023

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The expenses associated with software maintenance and evolution constitute a significant portion, surpassing more than 80% of the overall costs involved in software development. Refactoring, a widely embraced technique, plays a crucial role in streamlining and minimizing maintenance activities and expenses. However, the effect of refactoring techniques on quality attributes presents inconsistent and conflicting findings, making it challenging for software developers to enhance software quality effectively. Additionally, the absence of a comprehensive framework further complicates the decision-making process for developers when selecting appropriate refactoring techniques aligned with specific design objectives. In light of these considerations, this research aims to introduce a novel framework for classifying refactoring techniques based on their measurable influence on internal quality attributes. Initially, an exploratory study was conducted to identify commonly employed refactoring techniques, followed by an experimental analysis involving five case studies to evaluate the effects of these techniques on internal quality attributes. Subsequently, the framework was constructed based on the outcomes of the exploratory and experimental studies, further reinforced by a multi-case analysis. Comprising three key components, namely the methodology for applying refactoring techniques, the Quality Model for Object-Oriented Design (QMOOD), and the classification scheme for refactoring techniques, this proposed framework serves as a valuable guideline for developers. By comprehending the effect of each refactoring technique on internal quality attributes, developers can make informed decisions and select suitable techniques to enhance specific aspects of their software. Consequently, this framework optimizes developers' time and effort by minimizing the need to weigh the pros and cons of different refactoring techniques, potentially leading to a reduction in maintenance activities and associated costs.

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“…They also found that identifying the "bad smells" caused due to coupling and cohesion can be separated without changing the code behavior. Alves et al [34] proposed RefDistiller is a static analysis approach designed to aid with manual refactoring examination. RefDistiller combined two methods: a predefined template to identify potentially missing edits while manually refactoring.…”

Section: Literature Surveymentioning

confidence: 99%

Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques

et al. 2022

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Background: Refactoring is changing a software system without affecting the software functionality. The current researchers aim i to identify the appropriate method(s) or class(s) that needs to be refactored in object-oriented software. Ensemble learning helps to reduce prediction errors by amalgamating different classifiers and their respective performances over the original feature data. Other motives are added in this paper regarding several ensemble learners, errors, sampling techniques, and feature selection techniques for refactoring prediction at the class level. Objective: This work aims to develop an ensemble-based refactoring prediction model with structural identification of source code metrics using different feature selection techniques and data sampling techniques to distribute the data uniformly. Our model finds the best classifier after achieving fewer errors during refactoring prediction at the class level. Methodology: At first, our proposed model extracts a total of 125 software metrics computed from object-oriented software systems processed for a robust multi-phased feature selection method encompassing Wilcoxon significant text, Pearson correlation test, and principal component analysis (PCA). The proposed multi-phased feature selection method retains the optimal features characterizing inheritance, size, coupling, cohesion, and complexity. After obtaining the optimal set of software metrics, a novel heterogeneous ensemble classifier is developed using techniques such as ANN-Gradient Descent, ANN-Levenberg Marquardt, ANN-GDX, ANN-Radial Basis Function; support vector machine with different kernel functions such as LSSVM-Linear, LSSVM-Polynomial, LSSVM-RBF, Decision Tree algorithm, Logistic Regression algorithm and extreme learning machine (ELM) model are used as the base classifier. In our paper, we have calculated four different errors i.e., Mean Absolute Error (MAE), Mean magnitude of Relative Error (MORE), Root Mean Square Error (RMSE), and Standard Error of Mean (SEM). Result: In our proposed model, the maximum voting ensemble (MVE) achieves better accuracy, recall, precision, and F-measure values (99.76, 99.93, 98.96, 98.44) as compared to the base trained ensemble (BTE) and it experiences less errors (MAE = 0.0057, MORE = 0.0701, RMSE = 0.0068, and SEM = 0.0107) during its implementation to develop the refactoring model. Conclusions: Our experimental result recommends that MVE with upsampling can be implemented to improve the performance of the refactoring prediction model at the class level. Furthermore, the performance of our model with different data sampling techniques and feature selection techniques has been shown in the form boxplot diagram of accuracy, F-measure, precision, recall, and area under the curve (AUC) parameters.

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Refactoring Inspection Support for Manual Refactoring Edits

Cited by 18 publications

References 40 publications

Refactoring Practices in the Context of Modern Code Review: An Industrial Case Study at Xerox

Refactoring Practices in the Context of Modern Code Review: An Industrial Case Study at Xerox

A Refactoring Classification Framework for Efficient Software Maintenance

Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques

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