Estimating Software Reliability Using Size-biased Modelling

TL;DR

This paper introduces a Bayesian size-biased GLMM approach to estimate software reliability and total bugs, effectively handling discrete testing data and providing accurate parameter estimates through simulations and real data applications.

Contribution

The paper presents a novel Bayesian size-biased GLMM for software reliability estimation that also estimates total bugs, offering a flexible framework for various real-life scenarios.

Findings

Model accurately estimates key parameters in simulations.

Application to empirical data demonstrates practical usefulness.

Flexible framework applicable beyond software testing.

Abstract

Software reliability estimation is one of the most active areas of research in software testing. Since time between failures (TBF) has often been challenging to record, software testing data are commonly recorded as test-case-wise in a discrete set up. We have developed a Bayesian generalised linear mixed model (GLMM) based on software testing detection data and a size-biased strategy which not only estimates the software reliability, but also estimates the total number of bugs present in the software. Our approach provides a flexible, unified modelling framework and can be adopted to various real-life situations. We have assessed the performance of our model via simulation study and found that each of the key parameters could be estimated with a satisfactory level of accuracy. We have also applied our model to two empirical software testing data sets. While there can be other fields of study for application of our model (e.g., hydrocarbon exploration), we anticipate that our novel modelling approach to estimate software reliability could be very useful for the users and can potentially be a key tool in the field of software reliability estimation.