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Bug Severity Prediction System Using XGBoost Framework
Mondreti V.,
Published in IEEE
The field of Bug Reporting and Triaging has of late been a hot area of study among researchers trying to improve system management techniques. There is an increasing importance for developers to consider and address the various issues faced by users in order to not only ensure the delivery of quality service but also to understand the performance of the system under real-life scenarios. Hence, in this project, there is an attempt to develop a system that can improve the process of handling bug reports submitted by users of a software. This will be done through Bug Severity Prediction using the eXtreme Gradient Boosting (XGBoost) algorithm and the inclusion of a class balancing function to offset the bias due to the presence of majority and minority classes. The project would also include a study on the work that has already been done along with a proposal of the system architecture, methodologies used and the various hardware and software requirements. The main aim of the project is to shed light on the advantages of developing a Bug Severity Prediction system that can help reduce the dependence on users for providing accurate information. With the help of models built based on the history of bug reports received till date, the system should be able to take up some of the responsibilities of the user reporting the bug. © 2020 IEEE.
About the journal
JournalData powered by TypesetProceedings of the 2020 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2020
PublisherData powered by TypesetIEEE
Open AccessNo