An Overview of the State of Machine Learning in Bug Report Summarization
DOI:
https://doi.org/10.26438/ijcse/v9i2.5356Keywords:
Bug Report,, Machine Learning,, Supervised Learning, Unsupervised Learning, ClassifiersAbstract
Bug Report is one of the most consulted software artifacts during the software evolution and maintenance process. Summarization is one of the approaches which is generally performed over them to perform Bug Report Analysis tasks like Duplicate Bug Report Analysis for Bug Triagers, Quick understanding of Bug Reports, Classification of Bug Reports into priorities, etc. Information Retrieval Techniques, Natural Language Processing Techniques, Machine Learning Techniques and Deep Learning Based Techniques have been successfully implemented for doing the task. Machine Learning is one of the very popular techniques which has been used by almost 70 percent of the researchers for performing the Bug Report Summarization task. Machine Learning is a very common technique which is used in context of Bug Report Summarization due to the fact that the Bug Reports are very domain-specific in nature .In this paper we have systematically analyzed the Machine Learning works used for Bug Report Summarization. We have chosen all the popular papers available through Springer, IEEE, ACM, ACL Anthology and Google Scholar.
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