A Severity Assessment of Python Code Smells

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Gupta, Aakanshi, Gandhi, Rashmi, Jatana, Nishtha, Jatain, Divya, Panda, Sandeep Kumar, Ramesh, Janjhyam Venkata Naga
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Behavioral sciences; Class Change Proneness; Classifiers; Code smell Severity; Codes; Cognitive Complexity Code Smell; Empirical analysis; Internet of Things; Maintenance; Measurement; Open-source software; Python; Software; Software development; Software engineering; Software maintenance; Source code; Sustainable software
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3327553

Presence of code smells complicate the source code and can obstruct the development and functionality of the software project. As they represent improper behavior that might have an adverse effect on software maintenance, code smells are behavioral in nature. Python is widely used for various software engineering activities and tends tool to contain code smells that affect its quality. This study investigates five code smells diffused in 20 Python software comprising 10550 classes and analyses its severity index using metric distribution at the class level. Subsequently, a behavioral analysis has been conducted over the considered modification period (phases) for the code smell undergoing class change proneness. Furthermore, it helps to investigate the accurate multinomial classifier for mining the severity index. It witnesses the change in severity at the class level over the modification period by mapping its characteristics over various statistical functions and hypotheses. Our findings reveal that the Cognitive Complexity of code smell is the most severe one. The remaining four smells are centered around the moderate range, having an average severity index value. The results suggest that the J48 algorithm was the accurate multinomial classifier for classifying the severity of code smells with 92.98% accuracy in combination with the AdaBoost method. The findings of our empirical evaluation can be beneficial for the software developers to prioritize the code smells in the pre-refactoring phase and can help manage the code smells in forthcoming releases, subsequently saving ample time and resources spent in the development and maintenance of software projects.