Abstract—This research is the presenting the development of
hybrid algorithms that is called DT-NB or Decision Tree –
Naïve Bayesian to predict about the software quality. Besides, it
can develop the new technique of data mining for software
industry or the current software engineering .Then, these
techniques to make the comparison are Decision Tree,
Rule-Based and Naïve Bayesian.Similarly, according to the
analysis result of making the comparison for quality planning, it
was found that the technique DT-NB had the correctness result
of96 percent .Additionally, it was found that according to the
analysis result of making comparison for quality assurance, the
technique of DT-NB had the correctness result with76percent .
On the same way, according to the result analysis of making
comparison of quality control, it was found that the technique of
DT-NB had the correctness result of29 percent.
Index Terms—Hybrid algorithms, software quality, business management.
Kattiya T. Yangyuen is with the Department in information Technology of Rattana Bundit University, Thailand (e-mail: firstname.lastname@example.org).
Vichuda Rattanapian is with Rattana Bundit University, Thailand (e-mail: Rattanapian@gmail.com).
Cite: Kattiya T. Yangyuen and Vichuda Rattanapian, "The Development of DT-NB Hybrid Algorithms for Classifying Some Defective Dataset Types for Software Quality Prediction," International Journal of Information and Education Technology vol. 10, no. 1, pp. 1-6, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).