Relevance Vector Machine for Code Smell Detection

Various approaches have been proposed to detect code smells, including machine learning models. However, there are still challenges to improving detection accuracy and selecting appropriate quality metrics. This research introduces the use of Relevance Vector Machine (RVM) for code smell detection,...

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Bibliographic Details
Main Authors: Putro, Hanson Prihantoro, Yuhana, Umi Laili, Yuniarno, Eko Mulyanto, Purnomo, Mauridhi Hery
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
code smells detection
Codes
Feature extraction
Kernel
machine learning
relevance vector machine
Relevance vector machines
Software development management
Software reliability
Source coding
Text mining
Vectors
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Summary:Various approaches have been proposed to detect code smells, including machine learning models. However, there are still challenges to improving detection accuracy and selecting appropriate quality metrics. This research introduces the use of Relevance Vector Machine (RVM) for code smell detection, especially Magic Value smells, which often affect source code readability and maintainability in a software development industry. This approach utilizes source code analysis using two source code representations (raw source code and token stream) and feature extraction using two text mining techniques (Bag of Words/BoW and Term Frequency-Inverse Document Frequency/TF-IDF). The experiment involves analyzing 983 records of labeled source code datasets. The results show that RVM can detect code smells with a very high level of accuracy, reaching 99.9% on some kernel configurations, also nearly perfect precision, and F1 scores. This paper contributes to introducing the use of RVM for detecting code smells in source code, explores source code representation and feature extraction techniques, and shows that RVM can achieve high detection accuracy.
ISSN:2378-363X
DOI:10.1109/INDIN58382.2024.10774521