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Many authors try to predict the details of defects in software systems, before they deliver it to the customer. The purpose is to attempt to predict the quality of software at different stages in the process of software development. This defect is the sum of the errors found in code, design, documentation, specific requirements, and bad fixes errors introduced when repairing prior defects. Different variables come into picture in this process. In this paper neural network model is used considering the volume of U.S paper work with sizes and domain. Eight form of software paperwork containing requirements, documentations and specifications of few function points that are taken as input. US average Defect Potentials per function points (sum of requirements, design, code document and bad fix Defects) of few function points as output of the neural network.
"Prediction of Defects using Function Points", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.2, Issue 6, page no.337 - 341, June-2017, Available :http://www.ijrti.org/papers/IJRTI1706061.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator