Text Mining to Support Predicting Severity of Incident Reporting: A Feasibility Study

• Jacson Rodrigues Barbosa USP / UFG
• Ivone Penque Matsuno USP / UFMS
• Eduardo R. Guimarães UFG
• Solange Oliveira Rezende USP
• Auri M. R. Vincenzi UFSCar
• Márcio E. Delamaro USP

DOI: https://doi.org/10.5753/sbqs.2017.15094

Abstract

Due to a large number of incident reports that are persistent in Bug Tracking Systems repositories and the need to prioritize them according to the type of severity, it is necessary to investigate tools that support the prediction of incident reports severity. Objective: Apply Text Mining (TM) techniques and learning methods to help the prediction of incident reports severity from their descriptions. Method: A viability study was conducted to evaluate the application of preprocessing techniques and classification methods. Results: The semi-supervised learning method TCBHN presented good performance concerning the other approaches. Conclusion: The use of two-way heterogeneous networks and semi-supervised classification methods for predicting the severity of incident reports are promising.

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