Publication Type:

Journal Article

Source:

Advances in Intelligent Systems and Computing, Springer Verlag, Volume 515, p.525-532 (2017)

ISBN:

9789811031526

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015957853&doi=10.1007%2f978-981-10-3153-3_52&partnerID=40&md5=df5dece69b95268bbb4fb336294a82c2

Keywords:

Association rules, Bug summary, Computation theory, FP growths, Intelligent computing, Natural language processing systems, Program debugging, Stemming, Stop word, Tokenization

Abstract:

Nowadays bugs are the commonly occurring problems in many types of software. In order to prevent from these issues, a detailed study of bugs is an essential thing. Bugs are classified based on their severity in corresponding bug repositories. Some of the bug repositories are Mozilla, Android, Google Chromium, etc. So finding the most frequently occurring bugs is the right solution for the software malfunctioning. Thus it can help developers to prevent those bugs in the next release of the software. In this paper, our main aim is the mining of bugs from the bug summary data in the bug repositories by applying FP-Growth, one of the best techniques for finding frequently occurring pattern using WEKA. © Springer Nature Singapore Pte Ltd. 2017.

Notes:

cited By 0; Conference of 5th International Conference on Frontiers in Intelligent Computing Theory and Applications, FICTA 2016 ; Conference Date: 16 September 2016 Through 17 September 2016; Conference Code:189629

Cite this Research Publication

K. Divyavarma, Remya, M., and Deepa, G., “An enhanced bug mining for identifying frequent bug pattern using word tokenizer and FP-growth”, Advances in Intelligent Systems and Computing, vol. 515, pp. 525-532, 2017.

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