10-05-2014, 03:41 PM
Prediction of Defect in Software through the Data Sets (Error Reports)by Probabilities Theorem-PDPT
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Abstract:
An accurate prediction of the number of defects in a software product during system testing contributes not only to the management of the system testing process but also to the estimation of the product’s required maintenance. Here, a new approach,
called is Prediction of Defect in Software through the Data Sets (Error Reports)by Probabilities Theorem-PDPT presented that computes an estimate of the total number of defects in an ongoing testing process. PDPT is based on estimation theory. Unlike many existing approaches, the technique presented here does not depend on historical data from previous projects or any assumptions about the requirements and/or testers’ productivity. It is a completely automated approach that relies only on the data collected during an ongoing testing process. This is a key advantage of the PDPT approach as it makes it widely applicable in different testing environments. Here, the PDPT approach has been evaluated using five data sets from large industrial projects and two data sets from the literature. In addition, a performance analysis has been conducted using simulated data sets to explore its behavior using different models for the input data. The results are very promising; they indicate the PDPT approach provides accurate estimates with as fast or better convergence time in comparison
to well-known alternative techniques, while only using defect data as the input.
Index Terms—Defect prediction, system testing, estimation theory, maximum likelihood estimator.