Optimization of the Bug Report Classification Using Genetic Algorithm
Gayathri P M1, Greeshma K Babu2, Deepa G3
1Gayathri P M, Department of Computer Science and IT, AmritaVishwaVidyapeetham/ Amrita School of Arts and Scineces Kochi, Ernakulam, India.
2Greeshma K Babu, Department of Computer Science and IT, Amrita Vishwa Vidyapeetham/ Amrita School of Arts and Scineces Kochi, Ernakulam, India.
3Deepa G, Department of Computer Science and IT, Amrita Vishwa Vidyapeetham/Amrita School of Artsand Scineces Kochi, Ernakulam, India.
Manuscript received on 21 April 2019 | Revised Manuscript received on 26 May 2019 | Manuscript published on 30 May 2019 | PP: 3468-3470 | Volume-8 Issue-1, May 2019 | Retrieval Number: A3085058119/19©BEIESP
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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: A bug report is an effective way of communicating the bugs among bug reporters and bug recipients. At the same time, bad bug reports are long, inefficient form of communication for all concerned and do not contain relevant information to resolve the problems. The misclassification in bug report is therefore a serious issue that scarifies the accuracy of bug reports. Here we propose an approach to merging text mining and NLP to identify bugs and nonbugs in a bug report. In this system, KNN and Info Gain are used to classify and Genetic algorithm are used to optimize and improve automatic bug prediction performance.
Index Terms: KNN (K Nearest Neighbour) Classifier, NLTK (Natural Language Tool Kit), Genetic Algorithm.
Scope of the Article: Classification