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Application of Machine Learning Techniques for Software Reliability Prediction (SRP)

Application of Machine Learning Techniques for Software Reliability Prediction (SRP)
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Author(s): Pradeep Kumar (Maulana Azad National Urdu University, India)
Copyright: 2017
Pages: 30
Source title: Ubiquitous Machine Learning and Its Applications
Source Author(s)/Editor(s): Pradeep Kumar (Maulana Azad National Urdu University, India)and Arvind Tiwari (DIT University, India)
DOI: 10.4018/978-1-5225-2545-5.ch006

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Abstract

Software reliability is a statistical measure of how well software operates with respect to its requirements. There are two related software engineering research issues about reliability requirements. The first issue is achieving the necessary reliability, i.e., choosing and employing appropriate software engineering techniques in system design and implementation. The second issue is the assessment of reliability as a method of assurance that precedes system deployment. In past few years, various software reliability models have been introduced. These models have been developed in response to the need of software engineers, system engineers and managers to quantify the concept of software reliability. This chapter on software reliability prediction using ANNs addresses three main issues: (1) analyze, manage, and improve the reliability of software products; (2) satisfy the customer needs for competitive price, on time delivery, and reliable software product; (3) determine the software release instance that is, when the software is good enough to release to the customer.

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