Abstract
Software Reliability Growth Model is a mathematical model of how the software reliability improves as faults are detected and repaired. In this paper we propose a control mechanism based on the cumulative quantity between observations of time domain failure data using mean value function of Goel-Okumoto model, which is based on Non Homogenous Poisson Process. The model parameters are estimated by a two step approach. Software reliability process can be monitored efficiently by using Statistical Process Control. Control charts are widely used for process monitoring. It assists the software development team to identify failures and actions to be taken during software failure process and hence, assures better software reliability.Â
Highlights
Software reliability is defined as the probability of failure-free software operation for a specified period of time in a specified environment (Musa et al, 1987; Lyu, 1996)
The given 30 inter failure times are plotted through the estimated mean value function against the failure serial order
We conclude that our method of estimation and the control chart are giving a +ve recommendation for their use in finding out preferable control process or desirable out of control signal
Summary
Software reliability is defined as the probability of failure-free software operation for a specified period of time in a specified environment (Musa et al, 1987; Lyu, 1996). If the point falls above the UCL, it indicates that the process average, or the failure occurrence rate, may have decreased which results in an increase in the time between failures This is an important indication of possible process improvement. If the plotted point falls below the LCL, it indicates that the process average, or the failure occurrence rate, may have increased which results in a decrease in the failure time. This means that process may have deteriorated and actions should be taken to identify and the causes may be removed. The actual acceptable false alarm probability should depend on the actual product or process (Gokhale and Trivedi, 1998)
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