Treffer: A Bacterial Foraging Algorithm with Random Forest Classifier for Detecting the Design Patterns in Source Code.

A program design can be understood by the developers through detecting the design patterns in object-oriented programming source code. The main advantage of the design pattern detection includes maintainability, understand-ability and reusability of object-oriented programs. An object-oriented program is used as the input to the design pattern detection techniques, which judges the candidate roles of the patterns. The existing pattern detection techniques use the machine learning (ML) techniques, namely Support Vector Machine (SVM) and Decision Tree with all feature metrics (more than 70 metrics) to identify patterns, which increases the computation time. To minimize the issues of using all feature metrics, an effective feature selection technique is used in the research study. The most important relevant features are selected by proposed Bacterial Foraging Algorithm (BFA) and given as input to ensemble classifiers namely SVM, Decision Tree and Random Forest (RF) classifier to design pattern detection. By using BFA technique, the method used only five metrics for the identification of patterns, where existing techniques uses 70 to 80 metrics for same pattern detection. The simulations are conducted to test the effectiveness of BFA with ensemble classifiers on the Python Software platform in terms of average accuracy and precision. The results stated that BFA-RF achieved 88.57% of average accuracy, where BFA-SVM technique achieved 81.43% of average accuracy, which shows the RF achieved better results among other ensemble classifiers. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Intelligent Engineering & Systems is the property of Intelligent Networks & Systems Society 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.)