Evaluation of competing software reliability predictions
IEEE Transactions on Software Engineering - Special issue on reliability and safety in real-time process control
Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Prediction of Software Reliability Using Connectionist Models
IEEE Transactions on Software Engineering
Handbook of software reliability engineering
Handbook of software reliability engineering
Neural networks for software reliability engineering
Handbook of software reliability engineering
Optimal Release Times for Software Systems with Scheduled Delivery Time Based on the HGDM
IEEE Transactions on Computers
A Software Cost Model with Warranty and Risk Costs
IEEE Transactions on Computers
Software Reliability
Software Reliability Engineered Testing
Software Reliability Engineered Testing
Neural Networks for Identification, Prediction, and Control
Neural Networks for Identification, Prediction, and Control
Optimal software release scheduling based on artificial neural networks
Annals of Software Engineering
Using Neural Networks in Reliability Prediction
IEEE Software
An Analysis of Competing Software Reliability Models
IEEE Transactions on Software Engineering
Optimal Release Time of Computer Software
IEEE Transactions on Software Engineering
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The GMDH (group method of data handling) network is an adaptive learning machine based on the principle of heuristic self-organization. In this paper, we apply the GMDH networks to predict software reliability in testing phase. Three kinds of networks: the basic GMDH and its improved versions based on PSS (prediction sum of squared) and AIC (Akaike information criterion), are introduced fro the prediction of the failure-occurrence times observed in testing phase of the software system. In numerical examples, the GMDH networks, the usual MLP (multi-layer perceptron) neural network and existing SRGMs (software reliability growth models) are compared from the viewpoint of predictive performance. It is shown that the GMDH networks can overcome the problem of determining a suitable network size in the use of an MLP neural network, and can provide a more accurate measure in the software reliability assessment than other prediction devices. Further, the problem to determine the optimal software release schedule, which minimizes the relevant expected total software cost, is considered in the framework of the GMDH network architecture.