全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Comparison of Hazard-Rates Considering Fault Severity Levels and Imperfect Debugging for OSS

DOI: 10.4236/jsea.2021.1411035, PP. 591-606

Keywords: Open Source Software, Bug Tracking System, Software Reliability, Hazard-Rate Model, Imperfect Debugging

Full-Text   Cite this paper   Add to My Lib

Abstract:

Software reliability model is the tool to measure the software reliability quantitatively. Hazard-Rate model is one of the most popular ones. The purpose of our research is to propose the hazard-rate model considering fault level for Open Source Software (OSS). Moreover, we aim to adapt our proposed model to the hazard-rate considering the imperfect debugging environment. We have analyzed the trend of fault severity level by using fault data in Bug Tracking System (BTS) and proposed our model based on the result of analysis. Also, we have shown the numerical example for evaluating the performance of our proposed model. Furthermore, we have extended our proposed model to the hazard-rate considering the imperfect debugging environment and showed numerical example for evaluating the possibility of application. As the result, we found out that performance of our proposed model is better than typical hazard-rate models. Also, we verified the possibility of application of proposed model to hazard-rate model considering imperfect debugging.

References

[1]  Tamura, Y. and Yamada, S. (2016) Comparison of Big Data Analyses for Reliable Open Source Software. Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, Bali, 4-7 December 2016, 1345-1349.
https://doi.org/10.1109/IEEM.2016.7798097
[2]  Yamada, S. and Tamura, Y. (2016) OSS Reliability Measurement and Assessment, Springer International Publishing, Cham.
https://doi.org/10.1007/978-3-319-31818-9
[3]  Tamura, Y. and Yamanda, 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, Istanbul, 10-13 October 2010, 720-726.
https://doi.org/10.1109/ICSMC.2010.5641839
[4]  Tamura, Y., Nobukawa, Y. and Yamada, S. (2015) A Method of Reliability Assessment Based on Neural Network and Fault Data Clustering for Cloud with Big Data. Proceedings of the 2nd International Conference on Information Science and Security, Seoul, 14-16 December 2015, 1-4.
https://doi.org/10.1109/ICISSEC.2015.7370965
[5]  Aljahdali, S.H., Sheta, A. and Rine, D. (2001) Prediction of Software Reliability: A Comparison between Regression and Neural Network Non-Parametric Models. Proceedings ACS/IEEE International Conference on Computer Systems and Applications, Beirut, 25-29 June 2001, 470-473.
https://doi.org/10.1109/AICCSA.2001.934046
[6]  Yamada, S. (2014) Software Reliability Modeling: Fundamentals and Applications. Springer-Verlag, Tokyo/Heidelberg.
[7]  Tamura, Y., Matsumoto, M. and Yamada, S. (2016) Software Reliability Model Selection Based on Deep Learning. Proceedings of the International Conference on Industrial Engineering, Management Science and Application 2016, Jeju Island, 23-26 May 2016, 1-5.
https://doi.org/10.1109/ICIMSA.2016.7504034
[8]  Tamura, Y. and Yamada, S. (2005) Comparison of Software Reliability Assessment Methods for Open Source Software. Proceedings of the 11th International Conference on Parallel and Distributed Systems, Vol. II, Fukuoka, 20-22 July 2005, 488-492.
[9]  Tamura, Y., Sone, H., Sugisaki, K. and Yamada, S. (2018) Effort Analysis of OSS Project Based on Deep Learning Considering UI/UX Design. Proceedings of the IEEE International Conference on Reliability, Infocom Technology and Optimization, Noida, 29-31 August 2018, 1-6.
https://doi.org/10.1109/ICRITO.2018.8748408
[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]  Jelinski, Z. and Moranda, P.B. (1972) Software Reliability Research. In: Freiberger, W., Ed., Statistical Computer Performance Evaluation, 465-484, Academic Press, New York, 465-484.
https://doi.org/10.1016/B978-0-12-266950-7.50028-1
[12]  Sandoqa, I., Alzghoul, F., Alsawalqah, H., Alzghoul, I., Alnemer, L. and Akour, M, (2016) Statistical Debugging Effectiveness as a Fault Localization Approach: Comparative Study. Journal of Software Engineering and Applications, 9, 412-423.
https://doi.org/10.4236/jsea.2016.98027
[13]  MDN Web Docs (2021) BugDetails.
https://developer.mozilla.org/
[14]  The Apache Software Foundation (2021) The Apache
HTTP Server Project.
https://bz.apache.org/bugzilla/
[15]  Yamada, S. and Sera, K. (1999) Imperfect Debugging Models with Two Kinds of Software Hazard Rate and Their Bayesian Formulation. The IEICE Transactions, J82-A, 1577-1584.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133