Abstract
Many software projects use bug tracking systems to collect and allocate the bug reports, but the priority assignment tasks become difficult to be completed because of the increasing bug reports. In order to assist developers to reduce the pressure on assigning the priority for each bug report, we propose an integration method to predict priority levels based on machine learning. Our approach considers the textual description in bug reports as features and feeds these features to three different classifiers. We utilize these classifiers to predict the bug reports with unknown type and obtain three different results. Simultaneously, we set weights to balance the abilities of identifying different categories based on the characteristics of different projects for each classifier. Finally, we utilize the weights to adjust prediction results and produce a unique priority for assigning to each bug reports. We perform experiments on datasets from 4 products in Mozilla and the experimental results show that our approach has a better performance in terms of identifying the priority of bug reports than previous general methods and ensemble methods.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (No. 61602077, No. 61672122), the Natural Science Foundation of Liaoning Province of China (No. 20170540097), and the Fundamental Research Funds for the Central Universities (No. 3132016348).
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Gao, G., Li, H., Chen, R., Ge, X., Guo, S. (2018). Identification of High Priority Bug Reports via Integration Method. In: Xu, Z., Gao, X., Miao, Q., Zhang, Y., Bu, J. (eds) Big Data. Big Data 2018. Communications in Computer and Information Science, vol 945. Springer, Singapore. https://doi.org/10.1007/978-981-13-2922-7_23
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DOI: https://doi.org/10.1007/978-981-13-2922-7_23
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