Analysis of Code Refactoring Impact on Software Quality: A Scientific Explanation

Kaur, Amandeep; Kaur, Manpreet

doi:10.9734/bpi/aaer/v7/7691d

Cited by 1 publication

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“…Refactoring can enhance the technology's exterior characteristics, such as renewability, maintainable code, and legibility 13 . Understanding the most common code smells, the most common refactoring strategies, and the program measurements that strongly affect them is essential to improving software quality 14 . In this situation, reformation is required to change legacy or deteriorated code into a more structured or modular form or migrate code to the various programming languages or language models 15 …”

Section: Introduction To Software Code Refactoringmentioning

confidence: 99%

“…13 Understanding the most common code smells, the most common refactoring strategies, and the program measurements that strongly affect them is essential to improving software quality. 14 In this situation, reformation is required to change legacy or deteriorated code into a more structured or modular form or migrate code to the various programming languages or language models. 15 The users usually define the rules manually, a tedious and challenging task.…”

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confidence: 99%

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Software code refactoring based on deep neural network‐based fitness function

K¹,

Thirumaaran²

2022

Concurrency and Computation

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Refactoring is extensively recognized for enhancing the internal structure of object-oriented software while preserving its external behavior. However, determining refactoring opportunities is challenging for designers and researchers alike. In recent years, machine learning algorithms have shown a great possibility of resolving this issue. This study proposes a deep neural network-based fitness function (DNNFF) to resolve the software refactoring issue. This study suggests an effective learning technique that automatically featured extracted from the trained models and predicted code clones to recommend which category to refactor. The software engineers automatically assess the recommended refactoring solutions using Genetic Algorithms (GA) for minimum iterations. A Deep Neural Networks (DNN) utilizes these training instances to assess the refactoring solutions for the residual iterations. The refactoring process primarily depends on software designers' skills and perceptions. The simulation findings demonstrate that the suggested DNNFF model enhances the code change score of 98.7%, automatic refactoring score of 97.3%, defect correlation ratio of 96.9%, refactoring precision ratio of 95.9%, flaw detection ratio of 94.4%, and reduces the execution time of 10.2% compared to other existing methods.

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Section: Introduction To Software Code Refactoringmentioning

confidence: 99%

mentioning

confidence: 99%