An Anti-Pattern Detection Technique Using Machine Learning to Improve Code Quality
| Autor: | Kazi Abu Taher, Shanto Rahman, Nazneen Akhter |
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| Rok vydání: | 2021 |
| Předmět: |
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Computer science business.industry media_common.quotation_subject Decision tree 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Software quality Spaghetti code Random forest Support vector machine Naive Bayes classifier 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Precision and recall computer media_common |
| Zdroj: | 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD). |
| DOI: | 10.1109/icict4sd50815.2021.9396937 |
| Popis: | Poor software design and coding tend the software programs to be buggy at a massive rate. To enhance the code quality this paper proposes an automatic anti-pattern detection technique, which identifies anti-patterns from source code using Machine Learning (ML) classifiers. Here, four anti-patterns are considered such as Blob, Feature Decomposition (FD), Swiss Army Knife (SAK) and Spaghetti Code (SC) from three open-source Java projects namely ArgoUML, Azureus and Xerces. To improve the accuracy, a data pre-processing technique namely SMOTE is adopted. To locate these anti-patterns, four ML classifiers have been used which are Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF) and Decision Tree (DT). The proposed technique shows a better performance in terms of three evaluation metrics such as precision, recall, f-measure. SVM with SMOTE performs better in terms of precision and recall that are respectively 96.42% and 96.18%. |
| Databáze: | OpenAIRE |
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