Notice of Violation of IEEE Publication Principles - An Artificial Immune Recognition System-based Approach to Software Engineering Management: with Software Metrics Selection

Abstract

Notice of Violation of IEEE Publication Principles<br><br>"An Artificial Immune Recognition System-based Approach to Software Engineering Management: with Software Metrics Selection"<br>by Xin Jin, Rongfang Bie, and X.Z. Gao<br>in the Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications, 2006, pp. 523-528<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br>This paper improperly paraphrased portions of original text from the paper cited below. The original text was paraphrased without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:<br><br>"Artificial Immune Recognition System (AIRS): A Review and Analysis"<br>by Jason Brownlee,<br>in Technical Report 1-02, Center for Intelligent Systems and Complex Processes (CISCP), Faculty of Information and Communication Technologies, Swinburne University of Technology, January 2005 <br/> Artificial immune systems (AIS) are emerging machine learners, which embody the principles of natural immune systems for tackling complex real-world problems. The artificial immune recognition system (AIRS) is a new kind of supervised learning AIS. Improving the quality of software products is one of the principal objectives of software engineering. It is well known that software metrics are the key tools in the software quality management. In this paper, we propose an AIRS-based method for software quality classification. We also compare our scheme with other conventional classification techniques. In addition, the gain ratio is employed to select relevant software metrics for classifiers. Results on the MDP benchmark dataset using the error rate (ER) and average sensitivity (AS) as the performance measures demonstrate that the AIRS is a promising method for software quality classification and the gain ratio-based metrics selection can considerably improve the performance of classifiers