[1]覃志武,谢晋雄,蔡伊娜,等.基于环境与神经网络的软件自适应建模[J].深圳大学学报理工版,2017,34(No.6(551-660)):570-576.[doi:10.3724/SP.J.1249.2017.06570]
 Qin Zhiwu,Xie Jinxiong,Cai Yina,et al.Software adaptive modeling method based on environment and neural network[J].Journal of Shenzhen University Science and Engineering,2017,34(No.6(551-660)):570-576.[doi:10.3724/SP.J.1249.2017.06570]
点击复制

基于环境与神经网络的软件自适应建模()
分享到:

《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第34卷
期数:
2017年No.6(551-660)
页码:
570-576
栏目:
电子与信息科学
出版日期:
2017-11-20

文章信息/Info

Title:
Software adaptive modeling method based on environment and neural network
文章编号:
201706004
作者:
覃志武1谢晋雄1蔡伊娜1闫毅宣12
1) 深圳市检验检疫科学研究院,广东深圳 518045
2) 河北师范大学数学与信息科学学院,河北石家庄050024
Author(s):
Qin Zhiwu1 Xie Jinxiong1 Cai Yi’na1 and Yan Yixuan12
1) Shenzhen Academy of Inspection and Quarantine, Shenzhen 518045, Guangdong Province, P.R.China
2) College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050024, Hebei Province, P.R.China
关键词:
软件工程软件自适应环境需求软件建模神经网络环境用例预测
Keywords:
software engineering software adaptive environment requirement software modeling neural network environment use case prediction
分类号:
TP 311
DOI:
10.3724/SP.J.1249.2017.06570
文献标志码:
A
摘要:
软件需求模型的建模是保证软件可靠运行的基础.传统方法在建模过程中对环境需求考虑较少,对环境的变化无法有效识别和合理应对,导致软件生命周期缩短.现有自适应建模过程属于被动感知需求,欠缺对未来可持续发展的需求加以预测和应对,同样无法延长软件生命周期.为了尽可能延长软件生命周期,提高重构开发效率,提出一种针对环境变化的软件自适应建模方法.该方法将软件运行所处环境作为需求单独分析处理,首先识别环境用例,其次构建环境用例并将功能指标进行量化处理,采用BP神经网络预测环境需求变化并作出应对策略.与HAN-YANG-XING模型比较,该方法可主动感知需求,对环境变化进行预测并做出适应性判断,有效延长软件生命周期.
Abstract:
The software requirement model is the basis to improve the development efficiency and ensure the reliable operation. However, the traditional methods can neither distinguish the environment requirements during the modeling process, nor effectively identify and give reasonable response to the environment changes. Meanwhile, the existing software adaptive modeling process belongs to passively sensing requirements and does not effectively predict and deal with future requirements. In order to solve the above-mentioned problems, a novel software adaptive modeling method is proposed to adapt to the environment changes. The method firstly utilizes the software environment in which the software is running as a separate requirement analysis. Then, the environment use case is identified and constructed and the function index is quantified. Finally, the BP neural network is used to predict the change of environment requirements and make the corresponding strategy. Compared with the HAN-YANG-XING model,the proposed method can actively sense requirements,predict the environmental changes and make adaptive judgments,which can effectively extend the software life cycle.

参考文献/References:

[1] Cheng B, Lemos R, Giese H, et al. Software engineering for self-adaptive systems: a research roadmap[M]. Berlin: Springer-Verlag, 2009.
[2] Yang Zhuoqun, Li Zhi, Jin Zhi, et al. A systematic literature review of requirements modeling and analysis for self-adaptive systems[C]// International Working Conference on Requirements Engineering: Foundation for Software Quality. Essen, Germany: Springer-Verlag, 2014: 55-71.
[3] 丁博, 王怀民, 史殿习. 构造具备自适应能力的软件[J]. 软件学报, 2013, 24(9):1981-2000.
Ding Bo, Wang Huaimin, Shi Dianxi. Constructing software with self-adaptability[J]. Journal of Software, 2013, 24(9): 1981-2000.(in Chinese)
[4] 杨帆, 蔡勋, 刘衡竹. 一种自适应可视化软件结构[J]. 系统仿真学报, 2006, 18(z1):333-335.
Yang Fang, Cai Xun, Liu Hengzhu. A sort of flexible visualization architecture[J]. Journal of System Simulation, 2006, 18(z1):333-335.(in Chinese)
[5] Dobson S, Sterritt R, Nixon P, et al. Fulfilling the vision of autonomic computing[J]. Computer, 2010, 43(1): 35-41.
[6] Ramirez A, Cheng B. Verifying and analyzing adaptive logic through UML state models[C]// IEEE International Conference on Software Testing, Verification and Validation. Lillehammer, Norway: IEEE, 2008:529-532.
[7] Luckey M, Gerth S, Soltenborn C, et al. QUAASY: quality assurance of adaptive systems[C]// ACM International Conference on Autonomic Computing. Karlsruhe, Germany: ACM, 2011:179-180.
[8] Luckey M, Engels G. High-quality specification of self-adaptive software systems[C]// The 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. San Francisco, USA: IEEE, 2013:143-152.
[9] Whittle J, Sawyer P, Bencomo N, et al. RELAX: incorporating uncertainty into the specification of self-adaptive systems[C]// IEEE International Conference on Requirements Engineering. Atlanta, USA: IEEE, 2009:79-88.
[10] Whittle J, Sawyer P, Bencomo N, et al. RELAX: a language to address uncertainty in self-adaptive systems requirement[J]. Journal of Requirements Engineering, 2010, 15(2): 177-196.
[11] 韩德帅, 杨启亮, 邢建春. 一种软件自适应UML建模及其形式化验证方法[J]. 软件学报, 2015, 26(4):730-746.
Han Deshuai, Yang Qiliang, Xing Jianchun. UML-based modeling and formal verification for software self-adaptation[J]. Journal of Software, 2015, 26(4): 730-746.(in Chinese)
[12] Chess D, Kephart J. The vision of autonomic computing[J]. IEEE Computer, 2003, 36(1):41-50.
[13] 刘淑梅, 薛庆禹, 黎贞发, 等. 基于BP神经网络的日光温室气温预报模型[J]. 中国农业大学学报, 2015, 20(1): 176-184.
Liu Shumei, Xue Qingyu, Li Zhenfa, et al. An air temperature predict model based on BP neural networks for solar greenhouse in North China[J]. Journal of China Agricultural University, 2015, 20(1): 176-184.(in Chinese)
[14] Li Jun, Mei Xue, Prokhorov D, et al. Deep neural network for structural prediction and lane detection in traffic scene[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 28(3): 690-703.
[15] Tan T, Qian Y,Yin M. Cluster adaptive training for deep neural network[C]// Icassp IEEE International Conference on Acoustics. Brisbane, Australia: IEEE, 2015:4325-4329.
[16] 熊伟, 李兵, 陈军, 等. 一种基于预测控制的SaaS系统自适应方法[J]. 计算机学报, 2016, 39(2):364-376.
Xiong Wei, Li Bing, Chen Jun, et al. A self-adaptation approach based on predictive control for SaaS[J]. Chinese Journal of Computers, 2016, 39(2): 364-376.(in Chinese)
[17] 刘春, 张伟, 赵海燕, 等. 基于反馈控制的软件适应性需求的识别与分析[J]. 软件学报, 2015, 26(4):713-729.
Liu Chun, Zhang Wei, Zhao Haiyan, et al. Software adaptation requirements identification and analysis based on feedback control[J]. Journal of Software, 2015, 26(4):713-729.(in Chinese)
[18] Cheng Zunshui, Li Dehao. Stability and hopf bifurcation of a three-layer neural network model with delays[J]. Neurocomputing, 2016, 175: 355-370.
[19] 深圳市市场监督管理局.SZDB/Z 183—2016 跨境电子商务通关 检验检疫系统架构[S].
Market and Quality Supervision Commission of Shenzhen Municipality. SZDB/Z 183—2016 Cross-border electronic commerce clearance-system architecture for inspection and quarantine[S].(in Chinese)
[20] 深圳市市场监督管理局.SZDB/Z 184—2016 跨境电子商务通关检验检疫业务流程[S].
Market and Quality Supervision Commission of Shenzhen Municipality. SZDB/Z 184—2016 Cross-border electronic commerce clearance-service process for inspection and quarantine[S].(in Chinese)

相似文献/References:

[1]梁正平,纪震,林佳利,等.基于三维编码的全流程食品追溯系统[J].深圳大学学报理工版,2010,27(3):312.
 LIANG Zheng-ping,JI Zhen,LIN Jia-li,et al.A whole course food tracing system based on three dimensional code[J].Journal of Shenzhen University Science and Engineering,2010,27(No.6(551-660)):312.
[2]章远阳.面向对象的思想及其设计方法学[J].深圳大学学报理工版,1992,(1-2):46.
 Zhang Yuanyang.Object-Oriented Idea and Its Design Methodology[J].Journal of Shenzhen University Science and Engineering,1992,(No.6(551-660)):46.

备注/Memo

备注/Memo:
Received:2017-05-20;Accepted:2017-07-29
Foundation:Science and Technology Project of General Administration of Quality Supervision, Inspection and Quarantine of China (2015IK048,2015IK254); Science and Technology Support Project of Certification and Accreditation Administration of China (2016RJWKJ018); Shenzhen Science and Technology Application Demonstration Project (KJYY201602291416 21130)
Corresponding author:Senior Engineer Cai Yi’na. E-mail: 1530210935@qq.com
Citation:Qin Zhiwu, Xie Jinxiong, Cai Yi’na, et al. Software adaptive modeling method based on environment and neural network[J]. Journal of Shenzhen University Science and Engineering, 2017, 34(6): 570-576.(in Chinese)
基金项目:国家质检总局科技计划资助项目(2015IK048,2015 IK254);国家认监委认证认可科技支撑计划资助项目(2016RJWKJ018);深圳市科技应用示范资助项目(KJYY20160229141621130)
作者简介:覃志武(1964—),男,深圳市检验检疫科学研究院高级工程师. 研究方向:软件工程与应用. E-mail:qzw@szciq.gov.com
引文:覃志武,谢晋雄,蔡伊娜,等. 基于环境与神经网络的软件自适应建模[J]. 深圳大学学报理工版,2017,34(6):570-576.
更新日期/Last Update: 2017-10-10