Graduate Project


Early stage software reliability prediction using Bayesian networks Public Deposited

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  • While much work has been done in estimating software reliability, little attention is paid to predict reliability as early as at the design time. In this report, we present our initial research results of building an early stage software reliability prediction model. In Part I, we will first investigate and extract essential factors related to early stage software reliability prediction. A reliability model is then proposed which incorporates software design metrics, software architecture specification, operational profiles, software development process and environment. The model is constructed in Bayesian networks to address the existence of uncertainty underlying the available design time information. In Part II, we will validate a small portion of the proposed reliability model. We first obtain various metrics values and test results on an object oriented student project. A small model is then generated by a specific algorithm, and various experiments are done on it. The prediction results are analyzed.
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