International Journal of Computer Science and Informatics IJCSI

ISSN: 2231-5292

ijcct journal

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IIMT Bhubaneswar

IJCSI

FAULT-PRONE COMPONENTS IDENTIFICATION FOR REAL TIME COMPLEX SYSTEMS BASED ON CRITICALITY ANALYSIS


D. JEYAMALA
Department of Computer Applications, Thiagarajar College of Engineering, Tamilnadu – 625 015,

S. BALAMURUGAN
Department of Computer Applications, Thiagarajar College of Engineering, Tamilnadu – 625 015,

A. JALILA
Department of Computer Applications, Thiagarajar College of Engineering, Tamilnadu – 625 015,

K. SABARI NATHAN
Department of Computer Applications, Thiagarajar College of Engineering, Tamilnadu – 625 015,


Abstract

Generally, complexity of Software affects the development and maintenance Cost. The Complexity of the software increases, when the number of Components increase, among these components, some are more critical than others which will lead to catastrophic effects on field use. Hence, it is needed to identify such critical components after coding to test them rigorously. In this paper, we presented a novel approach that helps to identify the critical components in the software based on Criticality Analysis. Criticality Analysis analyzes the critical value of each component based on their Sensitivity and Severity metrics

Recommended Citation

[1] Roger S. Pressman, Software Engineering, A Practitioner’s Approach, 5th ed. McGraw Hill, 1997. [2] Ohlsson, Niclas, M. Helander, and C. Wohlin, "Quality improvement by identification of fault-prone modules using software design metrics", in Proc. International Conference on Software Quality., pp. 1-13, 1996. [3] Hendry, Sallie, Kafura Dennis, “Software Structure Metrics Based on Information Flow,” IEEE Transactions on Software Engineering., Vol. 7, No. 5, pp. 510-518, 1981. [4] Christof Ebert, “Classification techniques for metric-based Software development,” Software Quality Journal., Vol.5, No.4, pp. 255-272. [5] Malhotra Ruchika, Jain Ankita, “Fault Prediction Using Statistical and Machine Learning Methods for Improving Software Quality,” Journal of Information Processing Systems., Vol.8, No.2, pp. 241- 262, 2012. [6] Shatnawi A Raed, Li Wei, “The Effectiveness of Software Metrics in Identifying Error-Prone Classes in Post-Release Software Evolution Process,” Journal of Systems and Software., Vol. 81, pp.1868 – 1882, 2008. [7] Czerwonka, Jacek, Rajiv Das, Nachiappan Nagappan, Alex Tarvo, and Alex Teterev, "Crane: Failure prediction

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