Software Engineering | Reliability Growth Models

The reliability growth group of models measures and predicts the improvement of reliability programs through the testing process. The growth model represents the reliability or failure rate of a system as a function of time or the number of test cases. Models included in this group are as following below.

  1. Coutinho Model –
    Coutinho adapted the Duane growth model to represent the software testing process. Coutinho plotted the cumulative number of deficiencies discovered and the number of correction actions made vs the cumulative testing weeks on log-log paper. Let N(t) denote the cumulative number of failures and let t be the total testing time. The failure rate, \lambda (t), the model can be expressed as

        $$\lambda (t)=\frac{N(t)}{t} $$ $$ =\beta_0t^{-\beta_1}$$

    where  \beta_0\: and\: \beta_1 are the model parameters. The least squares method can be used to estimate the parameters of this model.

  2. Wall and Ferguson Model –
    Wall and Ferguson proposed a model similar to the Weibull growth model for predicting the failure rate of software during testing. The cumulative number of failures at time t, m(t), can be expressed as

        $$m(t)=a_0[b(t)]^\beta $$

    where  \alpha_0\: and\: \alpha_1 are the unknown parameters. The function b(t) can be obtained as the number of test cases or total testing time. Similarly, the failure rate function at time t is given by

        $$\lambda (t)= {m^' (t)} = {a_0\beta b^' (t){[b(t)]^{\beta -1}}}$$

    Wall and Ferguson tested this model using several software failure data and observed that failure data correlate well with the model.

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