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Background/Objectives: There is a need to have tools/mechanisms to detect whether a software is reliable or not. Much time is spent while using Classical Hypothesis testing because the conclusions can be drawn only after collecting large amounts of data. Methods/Statistical Analysis: By adopting Sequential Analysis of Statistical science, such decision can be made quickly. In this paper, we proposed a new type of statistical science procedure, Sequential Probability Ratio Test (SPRT) applied for Burr Type XII model based on Time domain data. Findings: For the proposed Burr type XII model, we applied the SPRT methodology on five real time software failure datasets that were borrowed from different software services. The result exemplifies that the model has given a decision of rejection for all the datasets. Therefore our findings state that all used datasets are unreliable. Application/Improvements: Applying SPRT procedure on datasets, we can come to an early conclusion of reliable/unreliable software.


Burr Type XII Model, ML Estimation, NHPP, Sequential Probability Ratio Test, Time Domain Data