An over-sampling method for analogy-based software effort estimation

This paper proposes a novel method to generate synthetic project cases and add them to a fit dataset for the purpose of improving the performance of analogy-based software effort estimation. The proposed method extends conventional over-sampling method, which is a preprocessing procedure for n-group classification problems, which makes it suitable for any imbalanced dataset to be used in analogy-based system. We experimentally evaluated the effect of the over-sampling method to improve the performance of the analogy-based software effort estimation by using the Desharnais dataset. Results show significant improvement to the estimation accuracy by using our approach. Categories and Subject Descriptors D.2.9 [Software Engineering]: Management

• Type: Conference
• Year: 2008
• Where: ESEM
• Authors: Yasutaka Kamei, Jacky Keung, Akito Monden, Ken-ichi Matsumoto
• Keywords: Analogy-based Software Effort, ESEM 2008, Over-sampling Method, Software Effort Estimation, Software Engineering