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Type: Artigo
Title: Bug report severity level prediction in open source software : a survey and research opportunities
Author: Gomes, Luiz Alberto Ferreira
Torres, Ricardo da Silva
Côrtes, Mario Lúcio
Abstract: The severity level attribute of a bug report is considered one of the most critical variables for planning evolution and maintenance in Free/Libre Open Source Software. This variable measures the impact the bug has on the successful execution of the software system and how soon a bug needs to be addressed by the development team. Both business and academic community have made an extensive investigation towards the proposal methods to automate the bug report severity prediction. This paper aims to provide a comprehensive mapping study review of recent research efforts on automatically bug report severity prediction. To the best of our knowledge, this is the first review to categorize quantitatively more than ten aspects of the experiments reported in several papers on bug report severity prediction. The mapping study review was performed by searching four electronic databases. Studies published until December 2017 were considered. The initial resulting comprised of 54 papers. From this set, a total of 18 papers were selected. After performing snowballing, more nine papers were selected. From the mapping study, we identified 27 studies addressing bug report severity prediction on Free/Libre Open Source Software. The gathered data confirm the relevance of this topic, reflects the scientific maturity of the research area, as well as, identify gaps, which can motivate new research initiatives. The message drawn from this review is that unstructured text features along with traditional machine learning algorithms and text mining methods have been playing a central role in the most proposed methods in literature to predict bug severity level. This scenario suggests that there is room for improving prediction results using state-of-the-art machine learning and text mining algorithms and techniques
Subject: Software - Manutenção
Aprendizado de máquina
Country: Países Baixos
Editor: Elsevier
Rights: Fechado
Identifier DOI: 10.1016/j.infsof.2019.07.009
Date Issue: Nov-2019
Appears in Collections:IC - Artigos e Outros Documentos

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