Predicting Software Reliability in Softwarized Networks

TL;DR

This paper introduces a framework for predicting software bugs and reliability in softwarized networks using SRGM, demonstrated on open source projects, with methods to improve prediction accuracy.

Contribution

It proposes a novel framework for software reliability prediction in softwarized networks and evaluates its effectiveness on real open source projects.

Findings

Prediction accuracy varies between projects.

Different methods can enhance prediction accuracy.

Framework effectively estimates bugs and reliability parameters.

Abstract

Providing high quality software and evaluating the software reliability in softwarized networks are crucial for vendors and customers. These networks rely on open source code, which are sensitive to contain high number of bugs. Both, the knowledge about the code of previous releases as well as the bug history of the particular project can be used to evaluate the software reliability of a new software release based on SRGM. In this work a framework to predict the number of the bugs of a new release, as well as other reliability parameters, is proposed. An exemplary implementation of this framework to two particular open source projects, is described in detail. The difference between the prediction accuracy of the two projects is presented. Different alternatives to increase the prediction accuracy are proposed and compared in this paper.