Abstract: Software bugs are defects or faults in computer programs or systems that cause incorrect or unexpected operations. These negatively affect software quality, reliability, and maintenance cost; therefore many researchers have already built and developed several models for software bug prediction. Till now, a few works have been done which used machine learning techniques for software bug prediction. The aim of this paper is to present comprehensive study on machine learning techniques that were successfully used to predict software bug. Paper also presents a software bug prediction model based on supervised machine learning algorithms are Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF) and Logistic Regression (LR) on four datasets. We compared the results of our proposed models with those of the other studies. The results of this study demonstrated that our proposed models performed better than other models that used the same data sets. The evaluation process and the results of the study show that machine learning algorithms can be used effectively for prediction of bugs.

Nasraldeen Alnor Adam Khleel and Károly Nehéz, “Comprehensive Study on Machine Learning Techniques for Software Bug Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 12(8), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120884

@article{Khleel2021,
title = {Comprehensive Study on Machine Learning Techniques for Software Bug Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120884},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120884},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {8},
author = {Nasraldeen Alnor Adam Khleel and Károly Nehéz}
}