Empirical analysis of search based algorithms to identify change prone classes of open source software

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• 作者：Ankita Bansal ^{ankita.bansal06@gmail.com}
• 关键词：Change proneness ; Metrics ; Object oriented paradigm ; Search based algorithms ; Software quality ; Empirical validation
• 刊名：Computer Languages, Systems & Structures
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：47
• 期：part_P2
• 页码：211-231
• 全文大小：1286 K
• 卷排序：47

文摘

Used hybridized search based algorithms to identify change prone classes. For empirical validation, two open source projects (Apache Rave,Commons Math) used. Assessed performance of search based algorithms using g-mean and accuracy. Machine learning models constructed and performance compared with hybridised models. Results showed that hybridised models outperformed machine learning models.