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DOI: 10.14569/IJACSA.2021.0120587
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A Machine Learning based Analytical Approach for Envisaging Bugs

Author 1: Anjali Munde

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 5, 2021.

  • Abstract and Keywords
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Abstract: A software imperfection is a shortcoming, virus, defect, mistake, breakdown or glitch in software that initiates it to establish an unsuitable or unanticipated result. The foremost hazardous components connected with a software imperfection that is not identified at an initial stage of software expansion are time, characteristic, expenditure, determination and wastage of resources. Faults appear in any stage of software expansion. Thriving software businesses emphasize on software excellence, predominantly in the early stage of the software advancement. In succession to disable this setback, investigators have formulated various bug estimation methodologies till now. Though, emerging vigorous bug estimation prototype is a demanding assignment and several practices have been anticipated in the text. This paper exhibits a software fault estimation prototype grounded on Machine Learning (ML) Algorithms. The simulation in the paper directs to envisage the existence or non-existence of a fault, employing machine learning classification models. Five supervised ML algorithms are utilized to envisage upcoming software defects established on historical information. The classifiers are Naïve Bayes (NB), Support Vector Machine (SVM), K- Nearest Neighbors (KNN), Decision Tree (DT) and Random Forest (RF). The assessment procedure indicated that ML algorithms can be manipulated efficiently with high accuracy rate. Moreover, an association measure is employed to evaluate the propositioned extrapolation model with other methods. The accumulated conclusions indicated that the ML methodology has an improved functioning.

Keywords: Software bug prediction; prediction model; data mining; machine learning; Naïve Bayes (NB); support vector machine (SVM); k-nearest neighbors (KNN); decision tree (DT); random forest (RF); python programming

Anjali Munde, “A Machine Learning based Analytical Approach for Envisaging Bugs” International Journal of Advanced Computer Science and Applications(IJACSA), 12(5), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120587

@article{Munde2021,
title = {A Machine Learning based Analytical Approach for Envisaging Bugs},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120587},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120587},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {5},
author = {Anjali Munde}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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