Entropy Based Software Reliability Analysis of Multi-Version Open Source Software

Abstract

The number of issues fixed in the current release of the software is one of the factors which decides the next release of the software. The source code files get changed during fixing of these issues. The uncertainty arises due to these changes is quantified using entropy based measures. We developed a Non-Homogeneous Poisson Process model for Open Source Software to understand the fixing of issues across releases. Based on this model, optimal release-updating using entropy and maximizing the active user's satisfaction level subject to fixing of issues up to a desired level, is investigated as well. The proposed models have been validated on five products of the Apache open source project. The optimal release time estimated from the proposed model is close to the observed release time at different active user's satisfaction levels. The proposed decision model can assist management to appropriately determine the optimal release-update time. The proposed entropy based model for issues estimation shows improvement in performance for 21 releases out of total 23 releases, when compared with well-known traditional software reliability growth models, namely GO model <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> and S-shaped model <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> . The proposed model is also found statistically significant.