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An Enhanced Bug Mining for Identifying Frequent Bug Pattern Using Word Tokenizer and FP-Growth

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Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 515))

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.

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Correspondence to G Deepa .

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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

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  • DOI: https://doi.org/10.1007/978-981-10-3153-3_52

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3152-6

  • Online ISBN: 978-981-10-3153-3

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