Software reliability model with optimal selection of failure data

Abstract

In the use of software reliability models it is not necessarily the case that all the failure data should be used to estimate model parameters and to predict failures. The reason for this is that old data may not be as representative of the current and future failure process as recent data. Therefore, it may be possible to obtain more accurate predictions of future failures by excluding or giving lower weight to the earlier failure counts. Although “data aging” techniques such as moving average and exponential smoothing are frequently used in other fields, such as inventory control, we did not find use of data aging in the various models we surveyed. One model that includes the concept of selecting a subset of the failure data is the Schneidewind Non- Homogeneous Poisson Process (NHPP) software reliability model. In order to use the concept of data aging, there must be a criterion for determining the optimal value of the starting failure count interval. We evaluated four criteria for identifying the optimal starting interval for estimating model parameters. Three of the criteria are novel. WOof these treat the failure count interval index as a parameter by substituting model functions for data vectors and optimizing on functions obtained from maximum likelihood estimation techniques. The third one uses weighted least squares to maintain constant variance in the presence of the decreasing failure rate assumed by the model. The fourth criterion is the familiar mean square error. Our research showed that significantly improved reliability predictions can be obtained by using a subset of the failure data, based on applying the appropriate criteria, and using the Space Shuttle On-Board software as an example.