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Artificial Neural Network Applications for Software Reliability Prediction

Om Artificial Neural Network Applications for Software Reliability Prediction

Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone modules and development efforts. The application of machine learning algorithm i.e. artificial neural networks application in software reliability prediction during testing phase as well as early phases of software development process is presented as well. Applications of artificial neural network for the above purposes are discussed with experimental results in this book so that practitioners can easily use ANN models for predicting software reliability indicators.

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  • Språk:
  • Engelska
  • ISBN:
  • 9781119223542
  • Format:
  • Inbunden
  • Sidor:
  • 313
  • Utgiven:
  • 1. september 2017
  • Mått:
  • 152x229x19 mm.
  • Vikt:
  • 590 g.
  Fri leverans
Leveranstid: 2-4 veckor
Förväntad leverans: 24. januari 2025
Förlängd ångerrätt till 31. januari 2025
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Beskrivning av Artificial Neural Network Applications for Software Reliability Prediction

Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone modules and development efforts. The application of machine learning algorithm i.e. artificial neural networks application in software reliability prediction during testing phase as well as early phases of software development process is presented as well. Applications of artificial neural network for the above purposes are discussed with experimental results in this book so that practitioners can easily use ANN models for predicting software reliability indicators.

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