5551296
doi
10.35940/ijeat.E1161.069520
oai:zenodo.org:5551296
Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)
Publisher
S. Prakasam
Associate Professor, Scsvmv University, Enathur, Kanchipuram, Tamil Nadu, India
Comparative Study of Software Defect Prediction and Analysis the Class using Machine Learning Method
V. Ruckmani
Assistant Professor, Voorhees College Vellore, Anna Salai, Kosapet, Vellore, Tamil Nadu, India.
issn:2249-8958
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Computational software engineering, identification, application software
<p>An automatic mode that increases sample stability is checked to verify the software design. Predict software flaws are the main focus of the engineering department. Computational software engineering is one of the active study areas of a software flaw. Depending on the metric, software quality and the efficient allocation of volume resources can easily improve defect quality, thus reducing costs. Many data mining and datasets can be used to store defect prediction software. Machine learning software defect prediction technology is an important branch of the computer. Therefore, in this method is to develop the defect prediction obtained by the design of selected class function metrics to create an effective error finding model. Various models have been proposed to reflect the changing changes in the software product's defect prediction index. These models also validate the data of the corresponding software module. The software defect analysis uses various software products for performance metrics to predict. It helps to find a different relationship between software volume and error size. Object classes are the user interface components in interactive applications. The control of the function property value assigned to the parsing code. The machine learning logic to detect errors due to defects. Advanced defect prediction models use different methods of performance class and function to evaluate. It provides a valid defect prediction for the defect identification code. This information is implemented in application software to improve predictive error classes and merit function code.</p>
Zenodo
2020-06-30
info:eu-repo/semantics/article
5551295
1633528107.225096
572891
md5:7df8273e745f92da369b776ebe4c81b2
https://zenodo.org/records/5551296/files/E1161069520.pdf
public
2249-8958
Is cited by
issn
International Journal of Engineering and Advanced Technology (IJEAT)
9
5
1313-1318
2020-06-30