Neural Networks Based Approach for Component Based Software Reliability Evaluation

We adapt concepts from the field of neural networks to assess the reliability of software, employing cumulative failures, reliability, remaining failures, and time to failure metrics. In addition, the risk of not achieving reliability, remaining failure, and time to failure goals are assessed. The purpose of the assessment is to compare a criterion, derived from a neural network model, for estimating the parameters of software reliability metrics, with the method of maximum likelihood estimation. To our surprise the neural network method proved superior for all the reliability metrics that were assessed by virtue of yielding lower prediction error and risk. We also found that considerable adaptation of the neural network model was necessary to be meaningful for our application – only inputs, functions, neurons, weights, activation units, and outputs were required to characterize our application.

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The scaling problem in neural networks for software reliability prediction

[1992] Proceedings Third International Symposium on Software Reliability Engineering ◽

10.1109/issre.1992.285856 ◽

2003 ◽

Cited By ~ 18

Author(s):

N. Karunanithi ◽

Y.K. Malaiya

Keyword(s):

Neural Networks ◽

Software Reliability ◽

Reliability Prediction ◽

Software Reliability Prediction

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AN EFFECTIVE EARLY SOFTWARE RELIABILITY PREDICTION PROCEDURE FOR PROCESS ORIENTED DEVELOPMENT AT PROTOTYPE LEVEL EMPLOYING ARTIFICIAL NEURAL NETWORKS

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539311004111 ◽

2011 ◽

Vol 18 (03) ◽

pp. 237-250 ◽

Cited By ~ 5

Author(s):

K. KRISHNA MOHAN ◽

A. K. VERMA ◽

A. SRIVIDYA

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Software Reliability ◽

Heterogeneous Systems ◽

Reliability Prediction ◽

Feed Forward Neural Network ◽

Distributed Components ◽

Software Reliability Prediction ◽

Artificial Neural ◽

Process Oriented

Reliability of a software product should be tracked during the software lifecycle right from the architectural phase to its operational phase. Heterogeneous systems consist of several globally distributed components, thus rendering their reliability evaluation more complex with respect to the conventional methods. In this context, reliability prediction of software process oriented systems assumes prime importance. It is important to take into account the proven processes like Rational Unified Process (RUP) to mitigate risks and increase the reliability of systems while building distributed based applications. This paper presents an algorithm using feed-forward neural network for early qualitative software reliability prediction. The inputs for neural networks consist of techno-complexity, practitioner's level, creation effort, size and leakage defects. The number of defects detected in each cycle can be predicted by using Artificial Neural Networks (ANN). Illustrative examples prove the effectiveness of the methodology employed.

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