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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

has been cited by the following article:

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

  AUTHORS: Taku Yanagisawa, Yoshinobu Tamura, Adarsh Anand, Shigeru Yamada

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

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.14 No.11, November 19, 2021

  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.