Performance Gauging of Discrete Classifiers by Prognostication of a Reported Bug
Mary Tannia Padua1, Amitha Joy2, G Deepa3
1Mary Tannia Padua*, PG Student, Department of Computer Science & IT, Amrita School of Arts and Sciences, Kochi, Amrita Vishwa Vidyapeetham, India.
2Amitha Joy, PG Student, Department of Computer Science & IT, Amrita School of Arts and Sciences, Kochi, Amrita Vishwa Vidyapeetham, India.
3G Deepa, Assistant Professor, Department of Computer Science and IT, Amrita School of Arts and Sciences, Kochi ,Amrita Vishwa Vidyapeetham, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on April 10, 2020. | PP: 654-656 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3407049620/2020©BEIESP | DOI: 10.35940/ijitee.F3407.049620
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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: Bugs are the frequently transpiring drawback in software’s, to prevent these problems; an in-depth study of bugs is required. Bug repositories are a significant supply of information. The bug repository helps the software team to have a better study about bugs and its related parameters. Often arising bugs helps the developers to get rid of them in upcoming releases. There is a huge variety of algorithms that facilitate in finding bugs. This paper intends to measure the performance of mining algorithms in predicting the bug severity by scrutinizing their capabilities. We propose a study of assorted algorithms in Lazy (IBK and KStar) and Tree (Random Forest) Classifiers with regard to different parameters. The case inspected here is Mozilla_ Thunderbird, which is implemented in WEKA.
Keywords: IBK, KStar, Random Forest, Resampling
Scope of the Article: Discrete Optimization.