Performance analysis of software reliability growth models with testing-effort and change-point
Journal of Systems and Software
Considering environmental function in reliability growth modeling from testing to operation
SEA '07 Proceedings of the 11th IASTED International Conference on Software Engineering and Applications
Quasi-renewal time-delay fault-removal consideration in software reliability modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
A cost analysis of systems subject to random field environments and reliability
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Software quality assurance using software reliability growth modelling: state of the art
International Journal of Business Information Systems
Research on software reliability considering testing profile and operational profile
CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Enhancing software reliability estimates using modified adaptive testing
Information and Software Technology
| Hi-index | 0.00 |
Software reliability growth models (SRGMs) have been developed to estimate software reliability measures such as the number of remaining faults, software failure rate, and software reliability. Issues such as imperfect debugging and the learning phenomenon of developers have been considered in these models. However, most SRGMs assume that faults detected during tests will eventually be removed. Consideration of fault removal efficiency in the existing models is limited. In practice, fault removal efficiency is usually imperfect. This paper aims to incorporate fault removal efficiency into software reliability assessment. Fault removal efficiency is a useful metric in software development practice and it helps developers to evaluate the debugging effectiveness and estimate the additional workload. In this paper, imperfect debugging is considered in the sense that new faults can be introduced into the software during debugging and the detected faults may not be removed completely. A model is proposed to integrate fault removal efficiency, failure rate, and fault introduction rate into software reliability assessment. In addition to traditional reliability measures, the proposed model can provide some useful metrics to help the development team make better decisions. Software testing data collected from real applications are utilized to illustrate the proposed model for both the descriptive and predictive power. The expected number of residual faults and software failure rate are also presented.