Growth model for detection and removal of faults having different severity with single change point and imperfect debugging

Asheesh Tiwari and Ashish Sharma

International Journal of Reliability and Safety, 2024, vol. 18, issue 1, 86-101

Abstract: Throughout the last decades, researchers have modelled a variety of software reliability growth models for estimating measures of reliability. In the present paper, we have classied faults into four divergent types as per their easiness and hardness in detection and removal. Also, variations in fault detection and correction rates can be because of the testing strategy, changing testing environment, motivation, proficiency and organisation of the debugging and testing teams, etc. In the present paper, a change point has been applied to four types of faults along with imperfect debugging during the correction of faults. This paper comprises two proposed software reliability growth models, which are compared on the basis of rate of detection as well as correction. All the model parameters are evaluated by the method of least squares. These models are assessed using various comparison measures like SSE, MSE, RMSE, Bias, variance and RMSPE.

Keywords: software reliability; software reliability growth model; change point; fault detection process; non-homogeneous Poisson process; sum of squared error; mean squared error; root mean square error; root mean square prediction error. (search for similar items in EconPapers)
Date: 2024
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