Prioritization of god class design smell: A multi-criteria based approach

Article Computer Science, Information Systems

A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class

Khalid Alkharabsheh et al.

Summary: This study investigates the effectiveness of machine learning approaches in software design smell detection, specifically focusing on God class detection. Results show that data balancing does not significantly influence the accuracy of detection, supporting the application of machine learning in detecting design smells in real scenarios.

INFORMATION AND SOFTWARE TECHNOLOGY (2022)

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Article Computer Science, Information Systems

Code Smell Prioritization with Business Process Mining and Static Code Analysis: A Case Study

Md Rofiqul Islam et al.

Summary: Code smells are a significant impediment to software maintainability. This study proposes a strategy to identify and prioritize frequently used and change-prone areas in a codebase by combining business logic, heat map information, and commit history analysis.

ELECTRONICS (2022)

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Article Computer Science, Software Engineering

Exploratory study of the impact of project domain and size category on the detection of the God class design smell

Khalid Alkharabsheh et al.

Summary: This study investigated the impact of project context information on machine learning techniques for detecting god class design smells, and found that feeding classifiers with this information can improve detection results.

SOFTWARE QUALITY JOURNAL (2021)

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Article Computer Science, Information Systems

Analysing Agreement Among Different Evaluators in God Class and Feature Envy Detection

Khalid Alkharabsheh et al.

Summary: Research on the detection of design smells has increased significantly in the past decade, but its adoption in real software development practice is not comparable to that of automatic refactoring tools. Empirical studies show a lack of consistency among different evaluators in detecting design smells, possibly due to the subjective nature of defining and identifying these smells.

IEEE ACCESS (2021)

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Proceedings Paper Computer Science, Hardware & Architecture