ECMC Rule based Software Module Defect Prediction using Support Vector Balanced Data
Kovuru Vijaya Kumar1, Ch GVN Prasad2
1Kovuru Vijaya Kumar*, Computer Science and Engineering department, Rayalaseema University, Kurnool, Andhra Pradesh, India.
2Ch GVN Prasad, Department of Computer Science & Engineering, Sri Indu College of Engg & Tech, Hyderabad. India.
Manuscript received on February 27, 2020. | Revised Manuscript received on March 14, 2020. | Manuscript published on March 30, 2020. | PP: 5219-5223 | Volume-8 Issue-6, March 2020. | Retrieval Number: F1103038620/2020©BEIESP | DOI: 10.35940/ijrte.F1103.038620
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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: Software module defect prediction (SDP) is one of the promising research area due to its wide range of application across multiple cross domain areas. Early detection of the defect module in a software reduces lot of effort put up in developing that software and it also reduces the cost associated in developing that software. Hence, in a quest to provide a better solution for SDP, we propose a novel rule based software module defect prediction using ECMC method in combination with support vector balanced data. It has been observed from our experiments that the results of this novel method are promising and satisfactory.
Keywords: Software Module Defect Prediction, ECMC, Rule-Based, Balancing Data.
Scope of the Article: Data Management.