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High-Dimensional Software Engineering Data and Feature Selection

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4 Author(s)
Huanjing Wang ; Western Kentucky Univ., Bowling Green, KY, USA ; Khoshgoftaar, T.M. ; Kehan Gao ; Seliya, N.

Software metrics collected during project development play a critical role in software quality assurance. A software practitioner is very keen on learning which software metrics to focus on for software quality prediction. While a concise set of software metrics is often desired, a typical project collects a very large number of metrics. Minimal attention has been devoted to finding the minimum set of software metrics that have the same predictive capability as a larger set of metrics - we strive to answer that question in this paper. We present a comprehensive comparison between seven commonly-used filter-based feature ranking techniques (FRT) and our proposed hybrid feature selection (HFS) technique. Our case study consists of a very high-dimensional (42 software attributes) software measurement data set obtained from a large telecommunications system. The empirical analysis indicates that HFS performs better than FRT; however, the Kolmogorov-Smirnov feature ranking technique demonstrates competitive performance. For the telecommunications system, it is found that only 10% of the software attributes are sufficient for effective software quality prediction.

Published in:

Tools with Artificial Intelligence, 2009. ICTAI '09. 21st International Conference on

Date of Conference:

2-4 Nov. 2009