A SOFTWARE RELIABILITY GROWTH MODEL WITH TESTING EFFORT DEPENDENT LEARNING FUNCTION FOR DISTRIBUTED SYSTEMS

doi:10.1142/s0218539304001579

A SOFTWARE RELIABILITY GROWTH MODEL WITH TESTING EFFORT DEPENDENT LEARNING FUNCTION FOR DISTRIBUTED SYSTEMS

2011-11-21

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DOI：10.1142/s0218539304001579

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Effective software process improvement will not start until management insists that product development work be planned and properly managed. This becomes even more challenging in an increasing number of major system developments made up from distributed sub-system software projects. These sub-systems are integrated and validated to provide the final system and product release. The need is growing to estimate, risk assess, plan and manage the development of these distributed sub-systems and the final full system release. In this paper, an attempt has been made to model the software reliability growth phenomenon with testing effort in a distributed development environment. Proposed Non Homogeneous Poisson Process (NHPP) based model assumes that the software system consists of a finite number of reused and newly developed sub-systems. The reused sub-systems do not consider the effect of severity of the faults on the software reliability growth phenomenon because they stabilize over a period of time i.e., the growth is uniform whereas, the newly developed sub-system do consider that. Fault removal phenomenon for reused and newly developed sub-systems have been modeled separately and is summed up to get the total fault removal phenomenon of the software system. The applicability of our model is shown by validating it on software failure data sets obtained from different real software development projects. The comparisons with established models in terms of goodness of fit, the Akaike Information Criterion (AIC), Mean of Squared Errors (MSE) have been presented.

A SOFTWARE RELIABILITY GROWTH MODEL WITH TESTING EFFORT DEPENDENT LEARNING FUNCTION FOR DISTRIBUTED SYSTEMS

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