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  Published Paper Details:

  Paper Title

Predicting A Faulty Parameter Using Machine Learning - An Analysis

  Authors

  Gaurav Kotecha,  Dimple Bisht,  Dipti,  Meghna Jha,  Rekha Jayaram

  Keywords

software fault prediction, knowledge based system, correlation based feature selection, prediction models, supervised machine learning

  Abstract


Today's software systems give us a wealth of information about both the points where the system performs as expected and the points where it fails. The knowledge base which can be established with the help of this data will help us in delivering an insight for any future act or manner of performance for the specified software. This research aims at looking for machine learning algorithms which can aid us in anticipating the behaviour of the software in consideration and hence also give an idea about the spots where the output of the system is not as desired,.i.e a defective parameter. This paper mainly looks over the creation of a knowledge base with the previously tested data and also predicts the areas and data values where a user can expect the system to be showing erroneous behavior.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2303883

  Paper ID - 233444

  Page Number(s) - h451-h456

  Pubished in - Volume 11 | Issue 3 | March 2023

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Gaurav Kotecha,  Dimple Bisht,  Dipti,  Meghna Jha,  Rekha Jayaram,   "Predicting A Faulty Parameter Using Machine Learning - An Analysis", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 3, pp.h451-h456, March 2023, Available at :http://www.ijcrt.org/papers/IJCRT2303883.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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