Revisiting Scenarios of Using Refactoring Techniques to Improve Software Systems Quality

Almogahed, Abdullah; Omar, Mazni; Zakaria, Nur Haryani; Muhammad, Ghulam; AlQahtani, Salman A.

doi:10.1109/access.2022.3218007

Cited by 13 publications

(22 citation statements)

References 72 publications

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“…It is recognized as a standard solution that focuses on restructuring the software's design structure while preserving its functionality [5], [6]. Consequently, software refactoring has emerged as a vital aspect of software maintenance and evolution [7], [8], becoming increasingly indispensable in the field of software development due to the evolving IT landscape and user requirements [3]. The significance of refactoring within software development processes has grown significantly, as it can be prompted by various factors such as new requirements, adaptation to diverse contexts, and subpar quality [3], [9].…”

Section: Introductionmentioning

confidence: 99%

“…In some cases, these effects may even be contradictory or opposing to one another [13], [30], [31], [32], [33]. Moreover, the particular sequence in which refactoring techniques are carried out can have different impacts on quality attributes [8], [33]. Consequently, it becomes challenging to differentiate the individual impacts of each refactoring technique or draw definitive conclusions about their impact on quality [13], [34].…”

Section: Introductionmentioning

confidence: 99%

“…Using refactoring techniques to improve software quality presents challenges for developers, given the inconsistent or contradictory results regarding their effects [8], [34]. Chaparro et al [35] highlight the difficulty developers face in assessing the advantages and disadvantages of refactoring techniques, which becomes even more complex when dealing with conflicting techniques.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The current lack of frameworks for software developers to identify appropriate refactoring techniques for achieving specific design goals has been noted [8], [39]. Developers require explicit guidance on the optimal path and refactoring choices to effectively achieve desired designs [8], [44]. Researchers are encouraged to explore various aspects in conjunction with refactoring, including the recommendation of specific refactoring techniques [13].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Refactoring Classification Framework for Efficient Software Maintenance

et al. 2023

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Software Refactoring Network: An Improved Software Refactoring Prediction Framework Using Hybrid Networking‐Based Deep Learning Approach

Pandiyavathi,

Sivakumar

2024

J Software Evolu Process

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Software refactoring plays a vital role in maintaining and improving the quality of software systems. The software refactoring network aims to connect developers, researchers, and practitioners to share knowledge, best practices, and tools related to refactoring. However, the network faces various challenges, such as the complexity of software systems, the diversity of refactoring techniques, and the need for automated and intelligent solutions to assist developers in making refactoring decisions. By leveraging deep learning techniques, the software refactoring network can enhance the speed, accuracy, and relevance of refactoring suggestions, ultimately improving the overall quality and maintainability of software systems. So, in this paper, an advanced deep learning–based software refactoring framework is proposed. The suggested model performs three phases as (a) data collection, (b) feature extraction, and (c) prediction of software refactoring. Initially, the data is collected from ordinary datasets. Then, the collected data is fed to the feature extraction stage, where the source code, process, and ownership metrics of all refactored and non‐refactored data are retrieved for further processing. After that, the extracted features are predicted using Adaptive and Attentive Dilation Adopted Hybrid Network (AADHN) techniques, in which it is performed using Deep Temporal Context Networks (DTCN) with a Bidirectional Long‐Short Term Memory (Bi‐LSTM) model. Here, the parameters in the hybrid networking model are optimized with the help of Constant Integer Updated Golden Tortoise Beetle Optimizer (CIU‐GTBO) for improving the prediction process. Therefore, the accuracy of the developed algorithm has achieved for different datasets, whereas it shows the value of 96.41, 96.38, 96.38, 96.38, 96.41, 96.38, and 96.39 for antlr4, junit, mapdb, mcMMO, mct, oryx, and titan. Also, the precision of the developed model has shown the better performance of 96.38, 96.32, 96.37, 96.33, 96.35, 96.37, and 96.31 for the datasets like antlr4, junit, mapdb, mcMMO, mct, oryx, and titan.

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Machine Learning-Based Exploration of the Impact of Move Method Refactoring on Object-Oriented Software Quality Attributes

Al Dallal,

Abdulsalam,

AlMarzouq

et al. 2023

Arab J Sci Eng

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Refactoring is a maintenance task that aims at enhancing the quality of a software’s source code by restructuring it without affecting the external behavior. Move method refactoring (MMR) involves reallocating a method by moving it from one class to the class in which the method is used most. Several studies have been performed to explore the impact of MMR on several quality attributes. However, these studies have several limitations related to the applied approaches, considered quality attributes, and size of the selected datasets. This paper reports an empirical study that applies statistical and machine learning (ML) approaches to explore the impact of MMR on code quality. The study overcame the limitations of the existing studies, and this improvement is expected to make the results of this study more reliable and trustworthy. We considered eight quality attributes and thirty quality measures, and a total of approximately 4 K classes from seven Java open-source systems were involved in the study. The results provide evidence that most of the quality attributes were significantly improved by MMR in most cases. In addition, the results show that a limited number of measures, when considered individually, have a significant ability to predict MMR, whereas most of the considered measures, when considered together, significantly contribute to the MMR prediction model. The constructed ML-based prediction model has an area under curve (AUC) value of 96.6%.

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Revisiting Scenarios of Using Refactoring Techniques to Improve Software Systems Quality

Cited by 13 publications

References 72 publications

A Refactoring Classification Framework for Efficient Software Maintenance

A Refactoring Classification Framework for Efficient Software Maintenance

Software Refactoring Network: An Improved Software Refactoring Prediction Framework Using Hybrid Networking‐Based Deep Learning Approach

Machine Learning-Based Exploration of the Impact of Move Method Refactoring on Object-Oriented Software Quality Attributes

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