Towards quantitative software reliability assessment in incremental development processes
Proceedings of the 33rd International Conference on Software Engineering
Estimating software reliability via pseudo maximum likelihood method
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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This paper proposes a multi-factor software reliability model based on logistic regression and its effective statistical parameter estimation method. The proposed parameter estimation algorithm is composed of the algorithm used in the logistic regression and the EM (expectation-maximization) algorithm for discrete-time software reliability models. The multi-factor model deals with the metrics observed in testing phase (testing environmental factors), such as test coverage and the number of test workers, to predict the number of residual faults and other reliability measures. In general, the multi-factor model outperforms the traditional software reliability growth model like discrete-time non-homogeneous models in terms of data-fitting and prediction abilities. However, since it has a number of parameters, there is the problem in estimating model parameters. Our modeling framework and its estimation method are quite simpler than the existing methods, and are promising for expanding the applicability of multi-factor software reliability model. In numerical experiments, we examine data-fitting ability of the proposed model by comparing with the existing multi-factor models. The proposed method provides the similar fitting ability to existing multi-factor models, although the computation effort of parameter estimation is low.