Applying Information Measure for Predicting Release Time of  Open Source Software

Talat PARVEEN; Hari Darshan ARORA

doi:10.48048/wjst.2018.2631

Authors

Talat PARVEEN Department of Applied Mathematics, Amity Institute of Applied Sciences, Amity University, Noida, Uttar Pradesh
Hari Darshan ARORA Department of Applied Mathematics, Amity Institute of Applied Sciences, Amity University, Noida, Uttar Pradesh

DOI:

https://doi.org/10.48048/wjst.2018.2631

Keywords:

Complexity of code change, entropy, prediction, software release, open source software

Abstract

Open Source Software (OSS) is updated regularly to meet the requirements posed by the customers. The source code of OSS undergoes frequent change to diffuse new features and update existing features in the system, providing a user friendly interface. The source code changes for fixing bugs and meeting user end requirements again affects the complexity of the code change and creates bugs in the software which are accountable to the next release of software. In this paper, the complexity of code changes in various Bugzilla open source software releases, from version 2.0 on 19^th Sep, 1998, to 5.0.1 on 10^th Sep, 2015, bugs in each software version release, and the time of release of each software version are considered, and the data used to predict the next release time. The Shannon entropy measure is used to quantify the code change process in terms of entropy for each software release. Observed code changes are utilized to quantify them into entropy units and are further used to predict the next release time. A neural network-based regression model is used to predict the next release time. The performance is compared with the R measure calculated using the multi linear regression model, and a goodness of fit curve is produced.

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Author Biography

Talat PARVEEN, Department of Applied Mathematics, Amity Institute of Applied Sciences, Amity University, Noida, Uttar Pradesh

Research Scholar. Department of Mathematics, Amity University, NOIDA, INDIA

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