Abstract
In this paper, Lehmann-Type Laplace Type I reliability growth model is proposed for early detection of software failure based on time between failure observations. Cumulative time between failures of the software data is assumed to follow Lehmann-Type Laplace distribution-Type I (LLD-Type I). The parameters are estimated using Profile Likelihood Method. In terms of AIC and BIC, this distribution is found to be a better fit for the software failure data than Goel–Okumoto, Weibull, Pareto Type III and Kumaraswamy Modified Inverse Weibull distributions which are commonly used in reliability analysis. A LLD-Type I control mechanism is used to detect the failure points of a software data.
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