Python code smells detection using conventional machine learning models

Code smells are poor code design or implementation that affect the code maintenance process and reduce the software quality. Therefore, code smell detection is important in software building. Recent studies utilized machine learning algorithms for code smell detection. However, most of these studies...

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Vydané v:PeerJ. Computer science Ročník 9; s. e1370
Hlavní autori: Sandouka, Rana, Aljamaan, Hamoud
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States PeerJ. Ltd 29.05.2023
PeerJ Inc
Predmet:
Algorithms
Analysis
Artificial Intelligence
Code smell
Data mining
Data Mining and Machine Learning
Detection
Java (Computer program language)
Large class
Long method
Machine learning
Python
Software Engineering
Large class
Code smell
Detection
Long method
Machine learning
Python
ISSN:2376-5992, 2376-5992
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Shrnutí:Code smells are poor code design or implementation that affect the code maintenance process and reduce the software quality. Therefore, code smell detection is important in software building. Recent studies utilized machine learning algorithms for code smell detection. However, most of these studies focused on code smell detection using Java programming language code smell datasets. This article proposes a Python code smell dataset for Large Class and Long Method code smells. The built dataset contains 1,000 samples for each code smell, with 18 features extracted from the source code. Furthermore, we investigated the detection performance of six machine learning models as baselines in Python code smells detection. The baselines were evaluated based on Accuracy and Matthews correlation coefficient (MCC) measures. Results indicate the superiority of Random Forest ensemble in Python Large Class code smell detection by achieving the highest detection performance of 0.77 MCC rate, while decision tree was the best performing model in Python Long Method code smell detection by achieving the highest MCC Rate of 0.89.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.1370