Software reliability models with time-dependent hazard function based on Bayesian approach

Authors:
L. Pham;H. Pham
Affiliations:
Dept. of Ind. Eng., Rutgers Univ., Piscataway, NJ;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Year:
2000

Citing 0
Cited 5

Software reliability forecasting by support vector machines with simulated annealing algorithms

Journal of Systems and Software
Software reliability identification using functional networks: A comparative study

Expert Systems with Applications: An International Journal
System reliability prediction model based on evidential reasoning algorithm with nonlinear optimization

Expert Systems with Applications: An International Journal
Warranty cost analyses using quasi-renewal processes for multicomponent systems

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Software reliability prediction model based on support vector regression with improved estimation of distribution algorithms

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, two models predicting mean time until next failure based on Bayesian approach are presented. Times between failures follow Weibull distributions with stochastically decreasing ordering on the hazard functions of successive failure time intervals, reflecting the tester's intent to improve the software quality with each corrective action. We apply the proposed models to actual software failure data and show they give better results under sum of square errors criteria as compared to previous Bayesian models and other existing times between failures models. Finally, we utilize likelihood ratios criterion to compare new model's predictive performance