Visualizing software refactoring using radar charts

Al-Ghuwairi, Abdel-Rahman; Al-Fraihat, Dimah; Sharrab, Yousef; Alrashidi, Huda; Almujally, Nouf; Kittaneh, Ahmed; Ali, Ahmed

doi:10.1038/s41598-023-44281-6

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…Furthermore, this approach aligns with standards to achieve strong and reliable findings when studying code smells and associated metrics within our dataset. It also ensures the accuracy and robustness of the experiments, allowing for more reliable and valid conclusions regarding code smells and their metrics [20], [37], [38].…”

Section: Methodsmentioning

confidence: 93%

“…Addressing feature envy bad smell though the move method refactoring is an effective approach that can be implemented to improve the quality of software code [17], [18]. The literature proposes various automatic refactoring methods to aid software specialists in identifying and rectifying problematic code through recommended refactoring operations [19], [20]. These methods can be rule-based, data-driven using machine learning, or software-based to optimize predetermined metrics [21].…”

Section: Introductionmentioning

confidence: 99%

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Detecting and resolving feature envy through automated machine learning and move method refactoring

Al-Fraihat,

Sharrab,

Al-Ghuwairi

et al. 2024

IJECE

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Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.

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Section: Methodsmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Detecting and resolving feature envy through automated machine learning and move method refactoring

Al-Fraihat,

Sharrab,

Al-Ghuwairi

et al. 2024

IJECE

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“…(2) 6) F-measure: is a commonly used metric for evaluating the success of classification techniques. To quantify this value, two measures called "Precision" and "Recall" must be computed, since the F-measure is the average of these two measures [29,30], as shown below:…”

Section: Figure 2 Roc Curvementioning

confidence: 99%

Hyperparameter Optimization for Software Bug Prediction Using Ensemble Learning

Al-Fraihat,

Sharrab,

Al-Ghuwairi

et al. 2024

IEEE Access

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Software Bug Prediction (SBP) is an integral process to the software's success that involves predicting software bugs before their occurrence. Detecting software bugs early in the development process enhances software quality, performance, and reduces software costs. The integration of Machine Learning (ML) algorithms has significantly improved software bug prediction accuracy and concurrently reduced costs and resource utilization. Numerous studies have explored the impact of Hyperparameter Optimization on single classifiers, enhancing these models' overall performance in SBP analysis. Ensemble Learning (EL) approaches have also demonstrated increased model accuracy and performance on SBP datasets. This study proposes a novel learning model for predicting software bugs through the utilization of EL and tuning hyperparameters. The results are compared with single hypothesis learning models using the WEKA software. The dataset, collected by the National Aeronautics and Space Administration (NASA) U.S.A., comprises 10,885 instances with 20 attributes, including a classifier for defects in one of their coding projects. The findings indicate that EL models outperform single hypothesis learning models, and the proposed model's accuracy increases after optimization. Furthermore, the accuracy of the proposed model demonstrates improvement following the optimization process. These results underscore the efficacy of ensemble learning, coupled with hyperparameter optimization, as a viable approach for enhancing the predictive capabilities of software bug prediction models.

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