What question did this study set out to answer?

This study aims to propose a supervised learning method using artificial neural networks for software reliability growth modeling.

April 28, 2026

A Supervised Learning Approach With ANN for Software Reliability Growth Modeling

Key Points

This study aims to propose a supervised learning method using artificial neural networks for software reliability growth modeling.
Utilized a polynomial artificial neural network to model fault rates.
Employed mean squared error (MSE) and variance for performance measurement.
Applied the model to real-world data for evaluation.
The suggested method outperformed existing software reliability growth models (SRGMs).
Demonstrated better predictive accuracy and reliability values compared to traditional approaches.

Abstract

ABSTRACT Software Reliability is necessary to determine the wellbeing of software and find the bugs. Software reliability growth models (SRGMs) are mathematical models that interpolate software failure rates and measure the progress made during the testing. This paper suggests a supervised learning approach that uses a polynomial artificial neural network in estimating fault rate. Mean squared error (MSE) and variance are used to measure the performance of the model in real‐world data. Findings have shown that the suggested method outperforms current SRGMs, and provides better predictive accuracy and reliability values.

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