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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Y, Zhou., Y, Tong., Gu, Ruihang., H, Gall.: Combining Text Mining and Data Mining for Bug Report Classification. ICSME. 311–320 (2014).
V, Neelima., N, Annapurna., V, Alekhya., B, Vidyavathi.: Bug Detection through Text Data Mining. IJARCSSE. 564–570 (2013).
shweta, M.S, Drgarg,.K.: Mining Efficient Association Rules Through Apriori Algorithm using Attributes and Comparative Analysis of Various Association Rule Algorithms. IJARCSSE. 306–312. (2013).
Rashmi, S., Prof nitin, S.: An Improved Association Rule Mining With Fp Tree Using Positive And Negative Integration. JGRCS. 46–51. (2012).
Yasmeen, S.: Software Bug Detection Algorithm using Data mining Techniques. IJIRAE. 105–108. (2014).
Drkanak, S., Rajpoot,..D..S..: A Way to Understand Various Patterns of Data Mining Techniques for Selected Domains. IJCSIS. 186–191. (2009).
marukatat, R.: On the Selection of Meaningful Association Rules. In Julio, P & adem, K (Eds), Data mining and knowledge discovery in real life applications pp. 75–88. (2009).
hahsler, M, chelluboina, S.: Visualising Association Rules : Introduction to the R-extension Package arulesViz.
kumar, K., Jayadev, .G, Narsimha, G.: Mining Frequent Patterns from Bug Repositories. IJARCSSE. 698–704. (2014).
Kanwal, J, Maqbool, O.: Managing Open Bug Repositories through Bug Report Prioritization Using SVMs. ICOSST. pp. 22–24. (2010).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Divyavarma, K., Remya, M., Deepa, G. (2017). An Enhanced Bug Mining for Identifying Frequent Bug Pattern Using Word Tokenizer and FP-Growth. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_52
Download citation
DOI: https://doi.org/10.1007/978-981-10-3153-3_52
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3152-6
Online ISBN: 978-981-10-3153-3
eBook Packages: EngineeringEngineering (R0)