Bug report severity level prediction in open source software: A survey and research opportunities

Autor(es)

Ferreira Gomes, Luiz Alberto ^[1] ; Torres, Ricardo da Silva ^[2] ; Cortes, Mario Lticio ^[2]

Afiliação do(s) autor(es)

^[1] Pontificia Univ Catolica Minas Gerais, Inst Exact Sci & Informat ICEI, Pocos De Caldas - Brazil

^[2] Univ Estadual Campinas, UNICAMP, Inst Comp, Campinas, SP - Brazil

• Tipo de documento: Artigo de Revisão
• Fonte: INFORMATION AND SOFTWARE TECHNOLOGY; v. 115, p. 58-78, NOV 2019.
• Citações Web of Science: 0

Resumo

Context: 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. Objective: 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. Method: 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. Results: 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. Conclusion: 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. (AU)

• Processo FAPESP: 14/50715-9 - Characterizing and predicting biomass production in sugarcane and eucalyptus plantations in Brazil
• Beneficiário: Rubens Augusto Camargo Lamparelli
• Linha de fomento: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
• Processo FAPESP: 16/50250-1 - O segredo de jogar futebol: Brasil versus Holanda
• Beneficiário: Sergio Augusto Cunha
• Linha de fomento: Auxílio à Pesquisa - Temático
• Processo FAPESP: 13/50169-1 - Towards an understanding of tipping points within tropical South American biomes
• Beneficiário: Ricardo da Silva Torres
• Linha de fomento: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Linha de fomento: Auxílio à Pesquisa - Temático
• Processo FAPESP: 17/20945-0 - EMU concedido no processo 16/50250-1: local positioning system
• Beneficiário: Sergio Augusto Cunha
• Linha de fomento: Auxílio à Pesquisa - Programa Equipamentos Multiusuários
• Processo FAPESP: 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems
• Beneficiário: Leonor Patricia Cerdeira Morellato
• Linha de fomento: Auxílio à Pesquisa - Programa de Pesquisa sobre Mudanças Climáticas Globais - PITE
• Processo FAPESP: 15/24494-8 - Comunicação e processamento de big data em nuvens e névoas computacionais
• Beneficiário: Nelson Luis Saldanha da Fonseca
• Linha de fomento: Auxílio à Pesquisa - Temático