Defect Prediction Model for Software Projects using Naïve Bayesian Classifier
Defect Prediction Model for Software Projects using Naïve Bayesian Classifier |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-9 |
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| Year of Publication : 2023 | ||
| Author : K. Suresh, K. Jayasakthi Velmurugan, S. Hemavathi, V. Kavitha |
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| DOI : 10.14445/22315381/IJETT-V71I9P216 | ||
How to Cite?
K. Suresh, K. Jayasakthi Velmurugan, S. Hemavathi, V. Kavitha, "Defect Prediction Model for Software Projects using Naïve Bayesian Classifier," International Journal of Engineering Trends and Technology, vol. 71, no. 9, pp. 170-177, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I9P216
Abstract
The objective of this paper is to examine how effective supervised learning mechanisms are in classifying the defective and non-defected software modules during the software development process by means of applying a Naïve Bayesian (NB) classifier. Defect in software modules is the main cause of crucial software project risks. In other words, high-quality software products can be achieved by applying the most significant risk management process. However, an organization's environment or the development of projects is severely affected by the presence of risk events. Some of the critical constraints such as resources, time or budget are damaged due to risk factors or risk. Major steps included in risk assessment techniques are i) identifying, ii) analyzing, iii) planning, and iv) controlling events that are affecting the project environment. In this work, a model can be developed using Machine Learning (ML) methods and its metric data for predicting the defective modules in the software project. The NB classifier used in this work classifies the predicted and non-predicted data based on the parameters to best suit complex real-time situations.
Keywords
Classification, Fuzzy decision-making trial and evaluation laboratory, Machine Learning, Naïve bayesian classifier, Support vector machine.
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