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Multi-Feature Approach for Bug Severity Assignment

Multi-Feature Approach for Bug Severity Assignment

Abeer Hamdy, Abdulrahman Ellaithy
Copyright: © 2020 |Volume: 11 |Issue: 2 |Pages: 15
ISSN: 1942-3926|EISSN: 1942-3934|EISBN13: 9781799806066|DOI: 10.4018/IJOSSP.2020040101
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MLA

Hamdy, Abeer, and Abdulrahman Ellaithy. "Multi-Feature Approach for Bug Severity Assignment." IJOSSP vol.11, no.2 2020: pp.1-15. http://doi.org/10.4018/IJOSSP.2020040101

APA

Hamdy, A. & Ellaithy, A. (2020). Multi-Feature Approach for Bug Severity Assignment. International Journal of Open Source Software and Processes (IJOSSP), 11(2), 1-15. http://doi.org/10.4018/IJOSSP.2020040101

Chicago

Hamdy, Abeer, and Abdulrahman Ellaithy. "Multi-Feature Approach for Bug Severity Assignment," International Journal of Open Source Software and Processes (IJOSSP) 11, no.2: 1-15. http://doi.org/10.4018/IJOSSP.2020040101

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Abstract

When bug reports are submitted through bug tracking systems, they are analysed manually to identify their severity levels. A severity level specifies the negative impact of a bug on a system. With the huge number of submitted reports, setting the severity class manually is tedious and time consuming. Moreover, some bug types are reported more often than other types, which leads to imbalanced bug repositories. This paper proposes a multi-feature approach for automatic severity assignment, which leverages lexical, semantic, and categorical properties of the bug reports. The proposed approach utilizes word embeddings, topic model, vector space model, and an adapted K-Nearest Neighbour technique. Moreover, the impact of utilizing two sampling techniques, namely SMOTE and cluster-based under-sampling (CBU), were investigated. Experiments over two open source repositories, Eclipse and Mozilla, demonstrated that the proposed approach is superior to two previous studies.

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