An Intelligent Code Smell Detection Technique Using Optimized Rule-Based Architecture for Object-Oriented Programmings

Kaur, Manpreet; Singh, Daljeet

doi:10.1007/978-981-16-8542-2_27

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…Automating the code smell detection and correction process is essential, especially in large software systems. In this regard, researchers have proposed various automated and semi-automated approaches supported with tools for code smell detection and correction, where they differ in their capabilities [2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Later, AbuHassan and Alshayeb [11] extended Misbhauddin's work and added four additional categories. The suggested seven categories for refactoring approaches are: Machine Learning (ML) and optimization-based [2,3], graph-based [4], software metricsbased [5], rule-based [6], token-based [7], text-based [8], and tree-based [9]. ML and optimization-based category has a wide range of subcategories that use statistical approaches, artificial intelligence, and different ML techniques to correct refactoring opportunities.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Automated Refactoring Tools

Alharbi,

Alshayeb

2024

IEEE Access

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Researchers proposed several refactoring approaches supported by automated and semiautomated refactoring tools. However, the existence of numerous automated refactoring tools imposes difficulties on developers to decide upon the appropriate one according to their needs. Moreover, the performance of the existing refactoring tools has not been empirically evaluated against the other available tools targeting the same refactoring opportunities. Therefore, the objective of this research is to conduct a comparative study to systematically compare and evaluate refactoring tools that belong to different categories of refactoring approaches. To this end, we propose an evaluation framework based on the DESMET methodology. The framework is used to empirically compare and evaluate four different refactoring tools, namely MultiRefactor, JDeodorant, jSparrow, and Spartenizer, using five open-source projects. The evaluation results show that jSparrow outperforms the other investigated tools by supporting the highest number of quantitative and qualitative features, suggesting that it is the best choice based on various perspectives. On the other hand, Spartenizer demonstrated the least favorable outcomes in terms of both quantitative and qualitative features, including introducing new code smells after applying a refactoring opportunity. The findings of this comparative study would assist the developers in understanding the characteristics and the capability of the studied refactoring tools. Also, it benefits the researchers to focus their efforts on addressing the identified limitations to enhance the smell detection and refactoring process.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Automated Refactoring Tools

Alharbi,

Alshayeb

2024

IEEE Access

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“…Code smells are categorized into different types based on the kind of bugs they can potentially cause. Here are some of the most common code smells [6]: long method, divergent change, parallel inheritance hierarchies, lazy class, incomplete library class, speculative generality, middleman, shotgun surgery, feature envy, long parameter list, comments, message chains, data clumps, inappropriate intimacy, large class, dead code, duplicate code, data class, object-orientation abusers, etc.…”

mentioning

confidence: 99%

“…Rulebased or static code analysis tools are increasingly popular [5]. By using intelligent code smell analysis and detection systems, the system would be able to adapt and update rules and sequences based on new changes, enabling it to predict or identify code smells in advance [6]. While many rulebased code analysis tools evaluate code based on predefined rules, they may struggle to incorporate new coding, design, or implementation standards.…”

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confidence: 99%

Improving Code Smell Detection Using Deep Stacked Autoencoder

Rehef,

Abbas

2024

Preprint

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The term "code smell" refers to an indication of a problem with the quality of source code. Numerous studies have been conducted to identify problematic features in source code. Initially, the focus was on utilizing metric-based and heuristic-based approaches. In recent years, however, there has been a shift towards using machine learning and deep learning (DL) techniques for smell detection. Nevertheless, the current algorithms are still considered to be in the early stages of development. Recognizing the challenges associated with identifying smells using DL methods, both academics and software developers have made efforts to address these obstacles. This work involves constructing and evaluating new DL models for code smell detection. Two models are built upon stacked autoencoders, employing a hybrid architecture that combines bidirectional long short-term memory and convolutional neural network components.

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A Hybrid Approach for Detecting Software Refactoring Sequencing

Maini,

Kaur

2024

Lecture Notes in Networks and Systems

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An Intelligent Code Smell Detection Technique Using Optimized Rule-Based Architecture for Object-Oriented Programmings

Cited by 3 publications

References 19 publications

A Comparative Study of Automated Refactoring Tools

A Comparative Study of Automated Refactoring Tools

Improving Code Smell Detection Using Deep Stacked Autoencoder

A Hybrid Approach for Detecting Software Refactoring Sequencing

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