Prediction of Software Design Defect using Enhanced Machine Learning Techniques
Karthikeyan C1, Makineni Vinay Chandra2, Jaswanth Santhosh Nadh3, Mellempudi Nikitha4
1Karthikeyan C, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
2Makineni Vinay Chandra, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
3Jaswanth Santhosh Nadh, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
4Mellempudi Nikitha, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 2462-2465 | Volume-8 Issue-5, January 2020. | Retrieval Number: E5725018520/2020©BEIESP | DOI: 10.35940/ijrte.E5725.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Prediction of software detection is most widely used in many software projects and this will improve the software quality, reducing the cost of the software project. It is very important for the developers to check every package and code files within the project. There are two classifiers that are present in the Software Package Defect (SPD) prediction that can be divided as Defect–prone and not-defect-prone modules. In this paper, the merging of Cost-Sensitive Variance Score (CSVS), Cost-Sensitive craniologist Score (CSLS) and Cost-Sensitive Constraint Score (CSCS). The comparitive analysis can be shown in between the three algorithms and also individually.
Keywords: Software prediction, CSLS, CSCS, CSVS.
Scope of the Article: Artificial Intelligence and machine learning.