Function point slicing model for IT system selfhealing

doi:10.3785/j.issn.1008973X.2009.09.002

2009, Vol. 43

Issue (09): 1557-1560 DOI: 10.3785/j.issn.1008973X.2009.09.002

Function point slicing model for IT system selfhealing

XU Ping, GAO Ji, ZHANG Qian-Li,GUO Hang

(College of Computer Science and Technology, Zhejiang University,Hangzhou 310027,China)

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Abstract

A function point slicing model (FPSM) based on the process model was proposed to solve the software selfhealing problem in the IT systems that lack databased analysis models and whose information is stored in nonstructural way. The model based on the five function components built different relationships within the components and the function point dependency graph, and then applied function point slicing to undertake the autonomic relation analysis in order to support selfhealing during the exception time of software running. The model realized the automation of exception cause determining, effect analysis of changes and solution matching. The experimental results indicate that the application of FPSM and FPSTool can reduce risk indicators and frequencies of exceptions．

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Cite this article:

XU Ping, GAO Ji, ZHANG Qian-Li, et al. Function point slicing model for IT system selfhealing. J4, 2009, 43(09): 1557-1560.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008973X.2009.09.002 OR http://www.zjujournals.com/eng/Y2009/V43/I09/1557

用于IT系统自修复的功能点切片模型

针对缺乏以数据为基础的分析模型和信息以非结构化形式存储的复杂IT系统中的软件自修复问题,提出了一个基于过程模型的功能点切片模型(FPSM).该模型在功能点分析5类组件的基础上,建立组件间的各种依赖关系,形成系统功能点依赖图,再应用功能点切片的方法,实现组件间的自动化关联分析,以支持软件系统运行阶段功能异常的自修复.模型实现了异常原因定位、变更影响分析以及解决方案匹配的自动化.实验结果表明,功能点切片模型和基于模型的切片器工具由于对系统组件进行了有效的定义和关联,能有效地降低异常事务的风险指标,并能够减小系统故障的发生频率．

[1] KEPHART J O, CHESS D M. The vision of autonomic computing[J]. IEEE Computer Society, 2003, 36(11):41-50.
[2] WHITE S R, HANSON J E. Autonomic computing: architectural approach and prototype [J]. Integrated Computer-Aided Engineering, 2006, 13(2):173-188.
[3] NAMI M R, BERTELS K. A survey of autonomic computing systems [C] ∥Proceedings of the 3rd International Conference on Autonomic and Autonomous Systems. New York: ACM, 2007: 91-101.
[4] GHOSH D, SHARMAN R, RAO H R, et al. Self-healing systems-survey and synthesis [J]. Decision Support Systems, 2007, 42(4):2164-2185.
[5] GORLA A. Towards design for self-healing [C] ∥ Fourth International Workshop on Software Quality Assurance. New York: ACM, 2007: 86-89.
[6] DUDLEY G, JOSHI N, OGLE D M, et al. Autonomic self-healing systems in a cross-product IT environment [C]∥ Proceedings of the International Conference on Autonomic Computing 2004. Washington: IEEE, 2004: 312-313.
[7] KOOPMAN P. Elements of the self-healing system problem space [C] ∥ Workshop on Architecting Dependable Systems 2003. Porland: [s.n.], 2003: 31-36.
[8] GARLAN D, SHMERL B. Model-based adaptation for self-healing systems [C] ∥ Proceedings of Workshop on Self-healing Systems 2002. New York: ACM, 2002: 27-32.
[9] LALA P K, KUMAR B K. On self-healing digital system design [J]. Microelectronics Journal, 2006, 37(4):353-362.
[10] BROWN A B, REDLIN C. Measuring the effectiveness of self-healing autonomic systems [C] ∥ Proceedings of the 2nd International Conference on Autonomic Computing. Seattle: IEEE, 2005: 328-329.[11] LEMOS R, FIADEIRO J L. An architectural support for self-adaptive software for treating faults [C] ∥ Proceedings of Workshop on Self-healing Systems 2002. New York: ACM, 2002: 39-42.
[12] RAO B, SARDA N L. Execution model for outsourced corrective maintenanc [C] ∥ Fifth International Conference on Computer and Information Technology. Ireland: IEEE, 2005: 944-948.

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