Analysis of Code Refactoring Impact on Software Quality

Kaur, Amandeep; Kaur, Manpreet

doi:10.1051/matecconf/20165702012

Cited by 20 publications

(10 citation statements)

References 9 publications

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“…As a result, the use of these tools sometimes negatively affects the quality of the code [34]. For example, Kaur and Kaur [51] used the Miner refactoring tool and found that the Move Method increased complexity. On the other hand, Chavez et al [52] used JDeodorant tool to apply this refactoring and found that the Move Method did not affect complexity.…”

Section: B Different Effects Of Refactoring Techniquesmentioning

confidence: 99%

Revisiting Scenarios of Using Refactoring Techniques to Improve Software Systems Quality

et al. 2023

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Refactoring is one of the most widely used techniques in practice to improve the quality of existing software. However, it is observed that refactoring does not continually improve all software quality attributes. Recent studies indicated that different refactoring techniques have significantly different, sometimes opposite, and conflicting effects on software quality attributes. In other words, there is contradictory evidence on the refactoring benefit. As a result, developers face challenges in selecting appropriate refactoring techniques when they use them to improve software quality. To the best of our knowledge, no study has investigated factors that may explain inconsistent or diverging results concerning the effect of refactoring techniques on software quality. Therefore, in this study, scenarios of using refactoring techniques factor have been identified, investigated, and thoroughly analyzed. Ten of the most commonly used refactoring techniques in practice have been chosen and individually applied in seven case studies of varying sizes (small, medium, and large). The Quality Model for Object-Oriented Design (QMOOD) is used to assess how refactoring techniques affect quality attributes. The findings provide strong evidence that this factor plays a significant role in producing the various effects of refactoring techniques on quality attributes. These findings can help software developers understand how to use refactoring techniques to improve software quality while taking this factor into account. The best scenario for using each refactoring technique to improve software system quality has been identified. The findings can provide guidelines for software developers to use refactoring techniques to improve the quality of software systems based on the best scenarios of using the refactoring techniques.

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Section: B Different Effects Of Refactoring Techniquesmentioning

confidence: 99%

Revisiting Scenarios of Using Refactoring Techniques to Improve Software Systems Quality

et al. 2023

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“…44 By refactoring code smells, we can improve the code quality in terms of reduction in complexity and maintenance cost. 45 Every code smell has its refactoring solution. 11 For example, Long Method code smell can be solved by using Extract Method refactoring, 46,47 Duplication in two unrelated classes can be solved using Extract Component, 27 and Shotgun Surgery, Feature Envy, Inappropriate Intimacy use Move Method and Move Field refactoring techniques.…”

Section: Alternative Classes With Different Interfacesmentioning

confidence: 99%

“…It cleans the code and makes it simple and readable 44 . By refactoring code smells, we can improve the code quality in terms of reduction in complexity and maintenance cost 45 . Every code smell has its refactoring solution 11 .…”

Section: Background and Related Workmentioning

confidence: 99%

Code smell prioritization in object‐oriented software systems: A systematic literature review

Verma

Kumar

Verma

2023

J Software Evolu Process

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Code smells indicate poor design and imperfection in coding style. They increase maintenance costs and degrade software quality. Detecting and mitigating these code smells reduce the effects of flaws present in the software design. However, to save time and effort, it is a good practice to prioritize critical code smells and resolve them first. There have been various empirical studies on code smell prioritization in the last decades. Although the research community has been continuously working on the prioritization of code smells, yet a systematic mapping of all these studies would provide a panorama of research work. With this motivation, we selected 39 primary studies published between May 2011 and May 2022 and classified them based on seven factors: (i) code smells, (ii) factors, (iii) evaluation techniques, (iv) tools, (v) ranking formulas, (vi) data sets used, and (vii) measurements used for validating the research work. This paper aims to outline and discuss the prioritization procedure for code smells in object‐oriented software systems. The issues and recommendations for every question act as input for the researchers and industrialists for further research. Some issues for further investigation are less coverage of architecture smells, limited ranking formulas, lack of automated evaluation techniques, and so forth.

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“…Code refactoring is the technique that restructures the code without changing its semantic behaviors [23], [24]. The initial purpose of code refactoring is to improve readability and reduce code complexity.…”

Section: B Code Refactoringmentioning

confidence: 99%

Enhancing Code Classification by Mixup-Based Data Augmentation

Dong¹,

Hu²,

Guo³

et al. 2022

Preprint

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Recently, deep neural networks (DNNs) have been widely applied in programming language understanding. Generally, training a DNN model with competitive performance requires massive and high-quality labeled training data. However, collecting and labeling such data is time-consuming and laborintensive. To tackle this issue, data augmentation has been a popular solution, which delicately increases the training data size, e.g., adversarial example generation. However, few works focus on employing it for programming language-related tasks. In this paper, we propose a Mixup-based data augmentation approach, MixCode, to enhance the source code classification task. First, we utilize multiple code refactoring methods to generate label-consistent code data. Second, the Mixup technique is employed to mix the original code and transformed code to form the new training data to train the model. We evaluate MixCode on two programming languages (JAVA and Python), two code tasks (problem classification and bug detection), four datasets (JAVA250, Python800, CodRep1, and Refactory), and 5 model architectures. Experimental results demonstrate that MixCode outperforms the standard data augmentation baseline by up to 6.24% accuracy improvement and 26.06% robustness improvement.

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Analysis of Code Refactoring Impact on Software Quality

Cited by 20 publications

References 9 publications

Revisiting Scenarios of Using Refactoring Techniques to Improve Software Systems Quality

Revisiting Scenarios of Using Refactoring Techniques to Improve Software Systems Quality

Code smell prioritization in object‐oriented software systems: A systematic literature review

Enhancing Code Classification by Mixup-Based Data Augmentation

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