基于模糊三角数模糊神经网络的软件质量评价方法

J4 ›› 2014, Vol. 36 ›› Issue (07): 1301-1306.

基于模糊三角数模糊神经网络的软件质量评价方法

李克文，张郁，马竟峰，刘洪太

（中国石油大学（华东）计算机通信与工程学院，山东青岛 266580）

收稿日期:2013-01-15 修回日期:2013-05-29 出版日期:2014-07-25 发布日期:2014-07-25
基金资助:
中央高校科研业务费专项资金资助项目(12CX04076A)；山东省自然科学基金资助项目(ZR2012HM060,ZR2013FL034)

Software quality evaluation method based on
fuzzy neural network with fuzzy triangle numbers

LI Kewen,ZHANG Yu,MA Jingfeng,LIU Hongtai

(College of Computer & Communication Engineering,China University of Petroleum,Qingdao 266580,China)

Received:2013-01-15 Revised:2013-05-29 Online:2014-07-25 Published:2014-07-25

摘要/Abstract

摘要：

用户对软件质量的评价与其体验密切相关，但由于软件产品的抽象性、复杂性以及用户需求的模糊性，目前软件质量评价方法都缺乏对该方面内容的关注，忽略了用户需求在软件质量评价中的作用。针对于此，考虑用户需求对软件质量的影响，将用户需求作为一种特殊的软件特性，构建了基于模糊三角数的模糊神经网络来处理软件开发过程中用户需求同软件特性之间的非线性关系，符合软件产品复杂性的特点，使软件质量评价结果更客观、全面。结果表明，基于模糊三角数模糊神经网络能够更好地反映用户需求同软件特性之间的非线性关系，是一种研究软件综合质量评价的有效方法。

关键词: 软件质量, 质量评价, 模糊神经网络, 模糊数

Abstract:

The results of software quality evaluation are closely related to users’ experience, but current software quality evaluation methods lack attention to user requirements because of the abstraction and complexity of software products and the fuzziness of customer requirements, thus ignoring the importance of user requirements on software quality evaluation. In consideration of the user requirements’ influence on the quality of software, a fuzzy neural network based on fuzzy numbers is proposed in order to imitate the nonlinear function between customer needs and software characteristics considering the influence of the user requirements on software quality, which can meet the complexity characteristics of software products, and make the results of software quality evaluation more objective and comprehensive. Results indicate that the method can more accurately imitate the nonlinear function between customer needs and software characteristics and is an effective measurement for studying software quality evaluation.

Key words: software quality;quality evaluation;fuzzy neural network;fuzzy numbers

李克文，张郁，马竟峰，刘洪太. 基于模糊三角数模糊神经网络的软件质量评价方法[J]. J4, 2014, 36(07): 1301-1306.

LI Kewen,ZHANG Yu,MA Jingfeng,LIU Hongtai. Software quality evaluation method based on
fuzzy neural network with fuzzy triangle numbers [J]. J4, 2014, 36(07): 1301-1306.

参考文献

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Table 2Software feature parameters when fuzzy parameter h=0.5表2模糊参数h=0.5时软件特性参数表截距x1x2x3x4x5x6x7x8y10.9124.6681.173-0.738(1.029)y22.6110.612(0.259)-1.0300.086(0.076)y30.7053.678y40.5541.1003.2480.893(0.296)y56.215-1.145(1.125)-2.1713.434-1.096x10.7190.028(0.507)x2-0.090(0.037)0.369-0.422-0.2251.350x30.508(0.422)0.453x40.153(0.455)1.074x50.327(0.517)0.552x60.392(0.829)0.194x70.147(0.230)0.738［10］Ishibuchi H,Kwon K,Tanaka H. A learning algorithm of fuzzy neural networks with triangular fuzzy weights［J］. Fuzzy Sets and Systems, 1995, 71(3):277293.
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附中文参考文献
［3］熊伟,王娟丽,蔡铭. 基于QFD技术的软件可信性评估研究［J］.计算机应用研究, 2013,24(2):397404.

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