Software reliability prediction incorporating information from a similar project

Abstract

Although there are many models for the prediction of software reliability using the failure data collected during testing, the estimation is usually inaccurate, especially at the early stages of the testing phase, and hence many practitioners are hesitant to use software reliability models. On the other hand, the traditional software reliability growth models do not make use of information from earlier or similar projects. For example, software systems today are usually an improvement or modification of an earlier version or at least within the same application domain, which implies that some information should be available from similar projects. In this paper we study some approaches for the estimation of software reliability by incorporating information from a similar project. In particular, we use the Goel-Okumoto model and assume the same value of the fault detection rate. The other parameter is then estimated based on the available testing data. For an actual set of data, our approach provides much more stable estimates and when the traditional maximum likelihood estimates exist and are reasonable, our results are very close to that from a statistical point of view. In addition, our approach does not require numerical algorithm to update the estimate and hence it is convenient to use.