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dc.contributor.advisor Ye, Xin en_US
dc.contributor.author Fang, Fan
dc.date.accessioned 2018-05-30T18:38:31Z
dc.date.issued 2018-05-30
dc.date.submitted 2018-05-15
dc.identifier.uri http://hdl.handle.net/10211.3/203272
dc.description.abstract Analyzing and processing bug reports can provide valuable information in software maintenance. However, some bug reports may contain unrelated information or just be a duplicated one. Thus, it will increase the efficiency to flter out those bug reports before performing analysis. In this paper, we propose a new approach for bug report classifcation, based on Recurrent Neural Networks (RNN), which will label bug reports as helpful or unhelpful. This approach achieves the purpose of reducing false positives and then improving the ranking performance by fltering out those unhelpful reports. Our model is tested over 9,000 bug reports from three software projects. The paper starts from how the input data is prepared for the model, how the model works, what the performance of the optimal model is, comparing with other previous models, and the evaluation of results. The evaluation result shows that our model helps improve a state-of-the-art IR-based system’s ranking performance under a trade-off between the precision and the recall. Our comparison experiments show that the RNN achieves the best trade-off between precision and recall than other classifcation models, including Convolutional Neural Network (CNN), multilayer perceptron, and a simple approach that classifies a bug report based on its length. In the situation that precision is more important than recall, our classifcation model helps for bug locating. en_US
dc.description.sponsorship Computer Science en_US
dc.language.iso en_US en_US
dc.subject Recurrent neural networks en_US
dc.subject Bug report en_US
dc.subject Long short-term memory en_US
dc.title Application of Recurrent Neural Networks for More Accurate Bug Reports Classifcation en_US
dc.description.embargoterms 3 years en_US
dc.date.embargountil 2021-05-29T18:38:31Z
dc.genre Project en_US
dc.contributor.committeemember Zhang, Xiaoyu en_US


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