An Experimental Analysis of Various Data Mining Techniques for Software Bug Classification
Raj Kumar1, Sanjay Singla2, Raj Kumar Yadav3, Dharminder Kumar4
1Raj Kumar, PhD Research Scholar, Department of CSE, IK Gujral Punjab Technical University, Jalandhar, Punjab, India.
2Dr. Sanjay Singla, Professor, Department of CSE, GGS College of Modern Technology Kharar (Mohali), Punjab, India.
3Dr. Raj Kumar Yadav, Associate Professor, Department of Computer Sc, Indira Gandhi University, Meerpur, Rewari, Haryana, India.
4Dr. Dharminder Kumar, Professor, Department of CSE, Guru Jambheshwar University of Science & Technology, Hisar, Haryana, India.
Manuscript received on 08 June 2019 | Revised Manuscript received on 13 June 2019 | Manuscript Published on 08 July 2019 | PP: 108-113 | Volume-8 Issue-8S3 June 2019 | Retrieval Number: H10300688S319/19©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: To make the human beings life easy, the use of software is increasing at day by day. The users of the software expect the early delivery of the software, so the demand to decrease the delivery time of software is increasing day by day. As the demand for early delivery of software in increasing day by day, so guaranteeing the quality of software is becoming critical. While designing and building the software there may be some errors which are commonly known as software bugs. About one third of the total cost is due to the software bugs. So it advantageous to use some intelligent technique for software bugs detection. The data of the software bug is contained in the repository, called the software bug repository. As the bug repository contains the huge amount of data, different types of data mining techniques may be applied to extract the hidden information from the software bug repository. Software bugs are classified using data mining techniques on the basis of the different parameters like accuracy precision, recall and F-measures. Different types of bug classification techniques using data mining have been studied in this paper and the results compared.
Keywords: Bug Tracking, Classification Algorithms, Data Mining, Software Bugs.
Scope of the Article: Classification