What is it about?
In this paper review of existing literature in the field of software reliability models based on machine learning techniques presented. Software reliability is very useful tool in determining the software quality. By using machine learning techniques for getting unhidden parameters affecting software fault prediction for exploring various parameters leading to obsoleteness of software by presenting category of papers of software reliability, software fault prediction, software trustworthiness, software reusability, using machine learning techniques based on statistical inferences which could predict useful pattern on hidden data of faulty software database of empirical datasets related to software testing. After studying plenary relevant papers on faults generated during fault removal, faults already present, we proposed a novel approach based on identifying most relevant parameter affecting the software reliability using Machine Learning Techniques.
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Why is it important?
Software Reliability, Intelligent Software, Machine Learning Techniques,Faults, Failures, Feature Selection
Perspectives
Writing this article was a great pleasure as it has co-authors with whom I have had long-standing collaborations. Reliability Prediction using Discrete Fourier Transform, the factor is calculated based on parameter model estimation.
MANU BANGA
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This page is a summary of: Implementation of Machine Learning Techniques in Software Reliability: A framework, April 2019, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/icactm.2019.8776830.
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