Open AccessSoftware Error Indication using Artificial Neural Network and Strong Back Propagation
Author(s) -
N Priya,
P Nandhini,
D Jeypriya,
Nikita Sharma,
D Jeyapriya
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i3185.0789s319
Subject(s) - artificial neural network , blame , computer science , mistake , metric (unit) , software , quality (philosophy) , product (mathematics) , correctness , artificial intelligence , field (mathematics) , machine learning , engineering , algorithm , operations management , mathematics , psychology , programming language , philosophy , geometry , epistemology , psychiatry , political science , pure mathematics , law
Software designing field contains different methodologies identified with expectation, for example, test exertion forecast, redress cost expectation, blame expectation and so on. Among these product blame expectation is the most mainstream look into zone and numerous new tasks are begun around there. At the point when there is a mistake in the PC program, it delivers an invalid or false outcome. Henceforth expectation of inadequate modules is important to improve the product quality. Different techniques and metric sets are accessible to discover the false modules that are blunder inclined. In this, Artificial Neural Network based programming flaw forecast method is utilized. To discover assessed answers for improvement and inquiry issues this technique is utilized. Manufactured Neural Network is utilized for finding the flawed components and additionally to predict the mistaken modules.