Optimal stopping time of software system test via artificial neural network with fault count data

Abstract

Purpose

The purpose of this paper is to present a novel method to estimate the optimal software testing time which minimizes the relevant expected software cost via a refined neural network approach with the grouped data, where the multi-stage look ahead prediction is carried out with a simple three-layer perceptron neural network with multiple outputs.

Design/methodology/approach

To analyze the software fault count data which follows a Poisson process with unknown mean value function, the authors transform the underlying Poisson count data to the Gaussian data by means of one of three data transformation methods, and predict the cost-optimal software testing time via a neural network.

Findings

In numerical examples with two actual software fault count data, the authors compare the neural network approach with the common non-homogeneous Poisson process-based software reliability growth models. It is shown that the proposed method could provide a more accurate and more flexible decision making than the common stochastic modeling approach.

Originality/value

It is shown that the neural network approach can be used to predict the optimal software testing time more accurately.

Citation

Begum, M. and Dohi, T. (2018), "Optimal stopping time of software system test via artificial neural network with fault count data", Journal of Quality in Maintenance Engineering, Vol. 24 No. 1, pp. 22-36. https://doi.org/10.1108/JQME-12-2016-0082