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BibTex Citation Data :
@article{JOIV1258,
author = {Amirul Zaim and Johanna Ahmad and Noor Hidayah Zakaria and Goh Eg Su and Hidra Amnur},
title = {Software Defect Prediction Framework Using Hybrid Software Metric},
journal = {JOIV : International Journal on Informatics Visualization},
volume = {6},
number = {4},
year = {2022},
keywords = {Software fault prediction; machine learning; object-oriented metric.},
abstract = {Software fault prediction is widely used in the software development industry. Moreover, software development has accelerated significantly during this epidemic. However, the main problem is that most fault prediction models disregard object-oriented metrics, and even academician researcher concentrate on predicting software problems early in the development process. This research highlights a procedure that includes an object-oriented metric to predict the software fault at the class level and feature selection techniques to assess the effectiveness of the machine learning algorithm to predict the software fault. This research aims to assess the effectiveness of software fault prediction using feature selection techniques. In the present work, software metric has been used in defect prediction. Feature selection techniques were included for selecting the best feature from the dataset. The results show that process metric had slightly better accuracy than the code metric.},
issn = {2549-9904}, pages = {921--930}, doi = {10.30630/joiv.6.4.1258},
url = {https://joiv.org/index.php/joiv/article/view/1258}
}
Refworks Citation Data :
@article{{JOIV}{1258}, author = {Zaim, A., Ahmad, J., Zakaria, N., Su, G., Amnur, H.}, title = {Software Defect Prediction Framework Using Hybrid Software Metric}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {6}, number = {4}, year = {2022}, doi = {10.30630/joiv.6.4.1258}, url = {} }Refbacks
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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JOIV : International Journal on Informatics Visualization
ISSN 2549-9610 (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
W : http://joiv.org
E : joiv@pnp.ac.id, hidra@pnp.ac.id, rahmat@pnp.ac.id
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is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.