Abstract
Reliability is the chief quality that one wishes for in anything. Reliability is also the main issue with computer systems. One of the purposes of system reliability analysis is to identify the weakness in a system and to quantify the impact of component failures. However, existing reliability prediction approaches for component-based software systems are limited in their applicability because they either neglect or do not support modeling explicitly several factors like error propagation, software fault tolerance mechanisms. In this paper, we evaluate reliability prediction of component-based system and fault tolerance structures technique by applying Pham Nordmann Zhang (PNZ) model, one of the best models based on non homogeneous Poisson process. Our approach uses a reliability modeling schema whose models are automatically transformed by a reliability prediction tool into PNZ models for reliability predictions and sensitivity analyses. Via these our case studies, we demonstrate its applicability and introduce how much reliability of software system can be improved by using fault tolerance structures technique.
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References
ISO/IEC-25010:2011: Systems and software quality requirements and evaluation (square) system and software quality models (square) (2011)
Rana, R.: Defect prediction & prevention in automotive software development (2013)
Roshandel, R.: Calculating architectural reliability via modeling and analysis. Ph.D. thesis, University of Southern California (2006)
Chengjie, X.: Availability and Reliability Analysis of Computer Software Systems Considering Maintenance and Security Issues. Ph.D. thesis (2011)
Brosch, F.: Integrated Software Architecture-Based Reliability Prediction for IT Systems, vol. 9. KIT Scientific Publishing, Karlsruhe (2012)
Larsson, M.: Predicting quality attributes in component-based software systems. Mälardalen University (2004)
Pham, T.-T., Defago, X.: Reliability prediction for component-based software systems with architectural-level fault tolerance mechanisms. In: Eighth International Conference on Availability, Reliability and Security, pp. 11–20. IEEE (2013)
Pham, H.: System Software Reliability. Springer, Heidelberg (2006)
Avizienis, A., Laprie, J.-C., Randell, B., Landwehr, C.: Basic concepts and taxonomy of dependable and secure computing. IEEE Trans. Dependable Secure Comput. 1(1), 11–33 (2004)
Pullum, L.L.: Software Fault Tolerance Techniques and Implementation. Artech House, Norwood (2001)
Pham, H., Nordmann, L., Zhang, Z.: A general imperfect-software-debugging model with s-shaped fault-detection rate. IEEE Trans. Reliab. 48(2), 169–175 (1999)
Avižienis, A.: Fault-tolerance and fault-intolerance: complementary approaches to reliable computing. In: ACM SIGPLAN Notices, vol. 10, pp. 458–464 (1975)
Acknowledgement
This research was supported by The National Foundation for Science and Technology Development (NAFOSTED) under Grant 102.03-2013.39: Automated verification and error localization methods for component-based software.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Binh, P., Quyet-Thang, H., Thanh-Hung, N., Hung-Cuong, N. (2016). Applying PNZ Model in Reliability Prediction of Component-Based Systems and Fault Tolerance Structures Technique. In: Vinh, P., Alagar, V. (eds) Context-Aware Systems and Applications. ICCASA 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 165. Springer, Cham. https://doi.org/10.1007/978-3-319-29236-6_27
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DOI: https://doi.org/10.1007/978-3-319-29236-6_27
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