A Comparative Study on New Classification Algorithm using NASA MDP Datasets for Software Defect Detection

Software Defect (SD) prediction motivates as basic position in upgrading software program and also it helps for software testing in reducing time as well as value of it. Defect prediction in software program is distinct as an enormously vital capacity when arranging a software task and considerably...

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Bibliographic Details
Published in:	2019 International Conference on Intelligent Sustainable Systems (ICISS) pp. 312 - 317
Main Authors:	Sreedevi, E, PremaLatha, V., Sivakumar, S., Nayak, Soumya Ranjan
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-02-2019
Subjects:	Accuracy Classification algorithms NASA NASA Dataset Predictive models Software Software Detection Software metrics
Online Access:	Get full text
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Summary:	Software Defect (SD) prediction motivates as basic position in upgrading software program and also it helps for software testing in reducing time as well as value of it. Defect prediction in software program is distinct as an enormously vital capacity when arranging a software task and considerably more endeavour is needed to comprehend this complicated problem utilizing a software measurements and defect dataset. Measurements are the association among the numerical and categorical attribute value and it applied at the software consequently its miles utilized for predicting defect. In this paper, we discussed about a study on new classification algorithm as know as Mixed Mode Database Miner (MMDBM) utilizing NASA MDP datasets. Finally the proposed algorithm MMDBM classifier progress to less processing time and high level of accuracy rate.
DOI:	10.1109/ISS1.2019.8908096