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Singh, V.B., Sharma, M. and Pham, H. (2017) Entropy Based Software Reliability Analysis of Multi-Version Open Source Software. IEEE Transactions on Software Engineering, 1207-1223.
https://doi.org/10.1109/TSE.2017.2766070

has been cited by the following article:

  • TITLE: OSS Project Assessment Based on Discriminant Analysis and Jump Diffusion Process Model for Fault Big Data

    AUTHORS: Yoshinobu Tamura, Hayato Watanabe, Shigeru Yamada

    KEYWORDS: Open Source Software, Big Fault Data, Discriminant Analysis, Open Source Project

    JOURNAL NAME: American Journal of Operations Research, Vol.10 No.6, November 9, 2020

    ABSTRACT: The bug tracking system is well known as the project support tool of open source software. There are many categorical data sets recorded on the bug tracking system. In the past, many reliability assessment methods have been proposed in the research area of software reliability. Also, there are several software project analyses based on the software effort data such as the earned value management. In particular, the software reliability growth models can apply to the system testing phase of software development. On the other hand, the software effort analysis can apply to all development phase, because the fault data is only recorded on the testing phase. We focus on the big fault data and effort data of open source software. Then, it is difficult to assess by using the typical statistical assessment method, because the data recorded on the bug tracking system is large scale. Also, we discuss the jump diffusion process model based on the estimation method of jump parameters by using the discriminant analysis. Moreover, we analyze actual big fault data to show numerical examples of software effort assessment considering many categorical data set.