Publisher : Advances in Intelligent Systems and Computing
Source : Advances in Intelligent Systems and Computing, Springer Verlag, Volume 515, p.525-532 (2017)
ISBN : 9789811031526
Keywords : Association rules, Bug summary, Computation theory, FP growths, Intelligent computing, Natural language processing systems, Program debugging, Stemming, Stop word, Tokenization
Campus : Kochi
School : School of Arts and Sciences
Department : Computer Science
Year : 2017
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.
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.