Abstract
System administrator deals with many problems, as computing environment becomes increasingly complex. Systems with an ability to recognize system states and adapt to resolve these problems offer a solution. Much experience and knowledge are required to build a self-adaptive system. Self-adaptive systems have inherent difficulties. This paper proposes a technique that automatically generates the code for the self-adaptive system. Thus the system is easier to build. Self-adaptive systems of previous research required high system resource usage. Incorrect operation could be invoked by external factors such as viruses. We propose an improved self-adaptive system approach and apply it to video conference system and robot system. We compared the lines of code, the number of classes created by the developers. We have confirmed this enhanced approach to be effective in reducing these development metrics.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Wang J, Guo C, Liu F. Self-healing based software architecture modeling and analysis through a case study. In Proc. Networking, Sensing and Control, Tucson, USA, Mar. 19–22, 2005, pp.873–877.
Autonomic Computing. IBM, http://www.research.ibm.com/autonomic.
Automating problem determination: A first step toward self-healing computing system. IBM White Paper, Oct. 2006.
Kephart J O, Chess D M. The visition of autonomic computing. IEEE Computer Society, 2004, 36(1): 41–50.
Laddaga R. Creating robust software through self-adaptation. IEEE Intelligent Systems, 2000, 15(6): 26–29.
Sommerville I. Software Engineering. Addison Wesley, 2007, pp.463–466.
Wile D S, Egyed A. An externalized infrastructure for self-healing systems. In Proc. the 4th Working IEEE/IFIP Confernce on Software Architecture, Oslo, Norway, Jun. 12–15, 2004, pp.285–288.
Park J, Lee J, Lee E. Goal graph based performance improvement for self-adaptive modules. In Proc. the 2nd ACM SIGKDD Int. Conference on Ubiquitous Information Management and Communication, Suwon, Korea, Feb. 30–31, 2008, pp.68–72.
Stelling P, DeMatteis C, Foster I, Kesselman C, Lee C, von Laszewski G. A fault detection service for wide area distributed computations. Cluster Computing, 1999, 2(2): 117–128.
Cleland-Huang J, Marrero W, Berenbach B. Goal-centric traceability: Using virtual plumblines to maintain critical systemic qualities. IEEE Trans. Software Engineering, 2008, 34(5): 685–699.
Garlan D, Schmerl B, Chang J. Using gauges for architecture-based monitoring and adaptation. In Proc. Working Conference on Complex and Dynamic Systems Architecture, Brisbane, Australia, Dec. 12–14, 2001.
Eclipse. http://www.eclipse.org, Oct. 8, 2010.
Garlan D, Cheng S W, Huang A C et al. Rainbow: Architecture-based self-adaptive with reusable infrastructure. IEEE Computer, 2004, 37(10): 46–54.
Park J, Youn H, Eunseok L. An automatic code generation for self-healing. Journal of Information Science and Engineering, 2009, 25(6): 1753–1781.
AspectJ. http://www.eclipse.org/aspectj, Oct. 8, 2010.
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by the Korean Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MEST) (No. 2009-0077453).
Rights and permissions
About this article
Cite this article
Lee, J., Park, J., Yoo, G. et al. Goal-Based Automated Code Generation in Self-Adaptive System. J. Comput. Sci. Technol. 25, 1118–1129 (2010). https://doi.org/10.1007/s11390-010-9393-2
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11390-010-9393-2