Analysis of Errors in Software Reliability Prediction Systems and Application of Model Uncertainty Theory to Provide Better Predictions

Models are the medium by which we reflect and express our understanding of some aspect of reality, a particular unknown of interest. As it is virtually impossible to grasp any situation in its entire complexity, models are representations of reality that are always partial resulting in a state of uncertainty or error. However the question of model error from a pragmatic point of view is not one of accounting for the difference between models and reality at a fundamental level, as such difference always exists. Rather the question is whether the prediction or performance of the model is correct at some practically acceptable level, within the model's domain of application. Here lays the importance of assessing the impact of uncertainties about predictions of a model, modeling the error and trying to reduce the uncertainties associated as much as possible to provide better estimations. While the methods for assessing the impact of errors on the performance of a model and error modeling are well established in various scientific and engineering disciplines, to the best of our knowledge no substantial work has been done in the field of Software Reliability Modeling despite the fact that the inadequacy of the present state and techniques of software reliability estimation has been recognized by industry and government agencies. In summary, even though hundreds of software reliability models have been developed, the software reliability discipline is still struggling to establish a software reliability prediction framework. This work intends to improve the performance of software reliability models through error modeling. It analyzes the errors associated with a set of five software Reliability Prediction Systems (RePSs) and attempts to improve their prediction accuracy using a model uncertainty framework. In the process, this work also statistically validates the performances of the RePSs. It also provides a time and cost effective alternative to performing experiments that are required to assess the error form which is integral to the process of application of the model uncertainty framework

Enhancing Accuracy of Software Reliability Prediction *

The measurement and prediction of software reliahility require the use of the Software Reliability Growth Models (SRGMs). The predictive quality can be measured by the average end-point projection er-ror L9]. In this paper, the effects of two orthogonal classes of approaches to improve prediction capability of a SRM have been examined using a large number of data sets. The first approach is preprocessing of data to filier out short term noise. The second is to over-come the bias inherent in the model. The results show that proper application of these two approaches can be more important than the selection of the model.

Enhancing Accuracy of Software Reliability Prediction

The measurement and prediction of software reliability require the use of the Software Reliability Growth Models (SRGMs). The predictive quality can be measured by the average end-point projection error [9]. In this paper, the effects of two orthogonal classes of approaches to improve prediction capability of a SRM have been examined using a large number of data sets. The first approach is preprocessing of data to filter out short term noise. The second is to overcome the bias inherent in the model. The results show that proper application of these two approaches can be more important than the selection of the model. 1 Introduction In order to achieve high reliability at an acceptable cost, developers need to be able to estimate the reliability of software under development, and, for management and planning purposes, they should be able to project the additional effort needed for their software to reach a certain reliability level. The reliability of software can be estimated statical..