Decision Tree for Smell Code Detection in Python: A Practical Implementation.
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| Názov: | Decision Tree for Smell Code Detection in Python: A Practical Implementation. |
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| Autori: | Ratnawati, Fajar, Jaroji |
| Zdroj: | EAI Computing & Communication in Emerging Regions - CCER; 2024, p1-9, 9p |
| Abstrakt: | This article discusses the approach to detect code that falls under the category of code smells in the Python programming language. The methodology involves reading each line of code and subsequently analyzing whether the program contains code smells. The code analysis is performed using a model trained with decision tree and J48 algorithms. The aim of this research, apart from developing previous research, is also to improve code quality and reduce security risks. Several aspects considered in this research include the number of parameters within a class, unused variables, duplicated print statements, and the number of classes within a code program. The constructed model utilizing the decision tree algorithm is then implemented into a web-based system, where input code for smell code identification is in the form of files with the extension .py. [ABSTRACT FROM AUTHOR] |
| Copyright of EAI Computing & Communication in Emerging Regions - CCER is the property of EAI - European Alliance for Innovation n.o. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Databáza: | Complementary Index |
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