Shinya Ikemoto
ABSTRACT
A mixed Poisson model with stochastic intensity is developed to describe the software reliability growth phenomena. where the software testing metrics depend on the intensity process. For such a generalized modeling framework, the common maximum likelihood method cannot be applied any more to the parameter estimation. In this paper, we propose to use the pseudo maximum likelihood method for the parameter estimation and to seek not only the model parameters but also the software reliability measures approximately. It is shown in numerical experiments with real software fault data that the resulting software reliability models based on four parametric approximations provide the better goodness-of-fit performance than the common non-homogeneous Poisson process models without testing metric information.
- T. Fujii, T. Dohi, and T. Pujiwara. Towards quantitative software reliability assessment in incremental development processes. In Proceedings of 33rd International Conference on Software Engineering (ICSE-2011), pages 123--456. ACM, 2011. Google Scholar
Digital Library
- A. L. Goel and K. Okumoto. Time-dependent error-detection rate model for software reliability and other performance measuress. IEEE Transactions on Reliability, R-28(3): 206--211, 1979.Google ScholarCross Ref
- C. Gourieroux, A. Monfort, and A. Trognon. Pseudo maximum likelihood methods: applications to Poisson models. Econometrica, 52: 701--720, 1984.Google ScholarCross Ref
- C. Gourieroux, A. Monfort, and A. Trognon. Pseudo maximum likelihood methods: theory. Econometrica, 52: 681--700, 1984.Google ScholarCross Ref
- M. R. Lyu. Handbook of Software Reliability Engineering. McGraw-Hill, New York, 1996. Google ScholarDigital Library
- J. D. Musa, A. Iannino, and K. Okumoto. Software Reliability, Measurement, Prediction, Application. McGraw-Hill, New York, 1987. Google ScholarDigital Library
- J. D. Musa and K. Okumoto. A logarithmic Poisson execution time model for software reliability measurement. In Proceedings of 7th Internaational Conference on Software Engineering (ICSE-1984), pages 230--238. ACM, 1984. Google ScholarDigital Library
- H. Okamura and T. Dohi. Software reliability modeling based on mixed Poisson distributions. International Journal of Reliability, Quality and Safety Engineering, 15(1): 9--32, 2008.Google ScholarCross Ref
- H. Okamura, Y. Etani, and T. Dohi. A multi-factor software reliability moel based on logistic regression. In Proceedings of 21st IEEE International Symposium on Software Reliability Engineering (ISSRE-2010), pages 31--40. IEEE CSP, 2010. Google ScholarDigital Library
- H. Okamura, Y. Etani, and T. Dohi. Quantifying the effectiveness of testing efforts on software fault detection with a logit software reliability growth model. In Proceedings of Joint Conference of the 21st International Workshop on Software Measurement (IWSM) and the 6th International Conference on Software Process and Product Measurement (Mensura). IEEE CSP, 2011. Google ScholarDigital Library
- H. Okamura, A. Murayama, and T. Dohi. EM algorithm for discrete software reliability models: a unified parameter estimation method. In Proceedings of 8th IEEE International Symposium on High Assurance Systems Engineering (HASE-2004), pages 219--228. IEEE CSP, 2004. Google ScholarDigital Library
- B. Ray, Z. Liu, and N. Ravishanker. Dynamic reliability models for software using time-dependent covariates. Technometrics, 4(1): 1--10, 2006.Google ScholarCross Ref
- K. Rinsaka, K. Shibata, and T. Dohi. Proportional intensity-based software reliability modeling with time-dependent metrics. In Proceedings of 30th Annual International Computer Software and Applications Conference (COMPSAC-2006), pages 405--410. IEEE CSP, 2006. Google ScholarDigital Library
- K. Shibata, K. Rinsaka, and T. Dohi. Metrics-based software reliability models using non-homogeneous Poisson processes. In Proceedings of 17th International Symposium on Software Reliability Engineering (ISSRE-2006), pages 52--61. IEEE CSP, 2006. Google ScholarDigital Library
- S. Yamada, M. Ohba, and S. Osaki. S-shaped reliability growth modeling for software error detection. IEEE Transactions on Reliability. R-32(5): 475--478, 1983.Google ScholarCross Ref
- S. Yamada and S. Osaki. Discrete software reliability growth models. Applied Stochastic Models and Data Analysis, 1: 65--77, 1985.Google ScholarCross Ref
- S. Yamada, S. Osaki, and H. Narihisa. Software reliability growth modeling with number of test runs. Transactions of the IECE of Japan, E67: 79--83, 1984.Google Scholar
Index Terms
- Estimating software reliability via pseudo maximum likelihood method
Recommendations
Toward a Software Testing and Reliability Early Warning Metric Suite
ICSE '04: Proceedings of the 26th International Conference on Software EngineeringThe field reliability is measured too late for affordablyguiding corrective action to improve the quality of thesoftware. Software developers can benefit from an earlywarning of their reliability while they can still affordablyreact. This early warning ...
Generalized Logit Regression-Based Software Reliability Modeling with Metrics Data
COMPSAC '13: Proceedings of the 2013 IEEE 37th Annual Computer Software and Applications ConferenceIt is well known that multifactor software reliability modeling with software metrics data is useful to predict the software reliability with higher accuracy, because it utilizes not only software fault count data but also software testing metrics data ...
Software Reliability Modeling with Software Metrics Data via Gaussian Processes
In this paper, we describe statistical inference and prediction for software reliability models in the presence of covariate information. Specifically, we develop a semiparametric, Bayesian model using Gaussian processes to estimate the numbers of ...
Comments