The software
reliability model is the stochastic model to measure the software reliability quantitatively. A Hazard-Rate Model is the well-known one as the typical software reliability model. We propose Hazard-Rate
Models Considering Fault Severity Levels (CFSL)
for Open Source Software (OSS). The purpose of this research is to make the Hazard-Rate Model considering CFSL adapt tobaseline
hazard function and 2 kinds of faults data in Bug Tracking System (BTS),i.e., we
use the covariate vectors in Cox proportional Hazard-Rate Model. Also, we show the numerical
examples by evaluating the performance of our proposed model. As the result, we compare the performance of our model with
the Hazard-Rate Model CFSL.
References
[1]
Tamura, Y. and Yamada, S. (2020) Large Scale Fault Data Analysis and OSS Reliability Assessment Based on Quantification Method of the First Type, Machine Learning and Knowledge Extraction, 2, 436-452. https://doi.org/10.3390/make2040024
[2]
Yamada, S. and Sera, K. (1999) Imperfect Debugging Models with Two Kinds of Software Hazard Rate and Their Bayesian Formulation. IEICE Transactions on Fundamentals, J82-A, 1577-1584. (in Japanese)
[3]
Tamura, Y. and Yamada, S. (2018) AI Approach to Fault Big Data Analysis and Reliability Assessment for Open-Source Software. In: Anand, A. and Ram, M., Eds., System Reliability Management: Solutions and Technologies, Advanced Research in Reliability and System Assurance Engineering. CRC Press Taylor & Francis Group, Boca Raton, 1-17. https://doi.org/10.1201/9781351117661-1
[4]
Tamura, Y. and Yamada, S. (2013) Reliability Assessment Based on Hazard Rate Model for an Embedded OSS Porting Phase. Software: Testing, Verification and Reliability, 23, 77-88. https://doi.org/10.1002/stvr.455
[5]
Tamura, Y. and Yamada, S. (2010) Software Reliability Analysis with Optimal Release Problems Based on Hazard Rate Model for an Embedded OSS. 2010 IEEE International Conference on Systems, Man and Cybernetics, 720-726. https://doi.org/10.1109/ICSMC.2010.5641839
[6]
Barack, O. and Huang, L. (2020) Assessment and Prediction of Software Reliability in Mobile Applications. Journal of Software Engineering and Applications, 13, 179-190. https://doi.org/10.4236/jsea.2020.139012
[7]
Jelinski, Z. and Moranda, P.B. (1972) Software Reliability Research. In: Freiberger, W. Ed., Statistical Computer Performance Evaluation, Academic Press, New York, 465-484. https://doi.org/10.1016/B978-0-12-266950-7.50028-1
[8]
Monrada, P.B. (1979) Event-Altered Rate Models for General Reliability Analysis. IEEE Transactions on Reliability, R-28, 376-381. https://doi.org/10.1109/TR.1979.5220648
[9]
Xie, M. (1989) On a Generalization of J-M Model. Proceedings of Reliability, 89, 5.
[10]
Schick, G.J. and Wolverton, R.W. (1978) An Analysis of Competing Software Reliability Models. IEEE Transactions on Software Engineering, SE-4, 104-120. https://doi.org/10.1109/TSE.1978.231481
[11]
Yanagisawa, T., Tamura, Y., Anand, A. and Yamada, S. (2021) Comparison of Hazard-Rates Considering Fault Severity Levels and Imperfect Debugging for OSS. Journal of Software Engineering and Applications, 14, 591-606. https://doi.org/10.4236/jsea.2021.1411035
[12]
Nishio, Y., Dohi, T. and Osaki, S. (2002) A Reliability Assessment of Software Product Based on Proportional Hazards Models. IEICE Transactions on Fundamentals, J85-A, 84-94.
[13]
Cox, D.R. (1972) Regression Models and Life Tables. Journal of the Royal Statistical Society, B-34, 187-220.
[14]
The Apache Software Foundation (2021) The Apache HTTP Server Project. http://httpd.apache.org/
