| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [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
Total Affiliations: 2
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| Document type: | Review article |
| Source: | INFORMATION AND SOFTWARE TECHNOLOGY; v. 115, p. 58-78, NOV 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
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) | |
| FAPESP's process: | 14/50715-9 - Characterizing and predicting biomass production in sugarcane and eucalyptus plantations in Brazil |
| Grantee: | Rubens Augusto Camargo Lamparelli |
| Support type: | Research Grants - Research Partnership for Technological Innovation - PITE |
| FAPESP's process: | 16/50250-1 - The secret of playing football: Brazil versus the Netherlands |
| Grantee: | Sergio Augusto Cunha |
| Support type: | Research Projects - Thematic Grants |
| FAPESP's process: | 13/50169-1 - Towards an understanding of tipping points within tropical South American biomes |
| Grantee: | Ricardo da Silva Torres |
| Support type: | Research Grants - Research Partnership for Technological Innovation - PITE |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support type: | Research Projects - Thematic Grants |
| FAPESP's process: | 17/20945-0 - Multi-user equipment approved in great 16/50250-1: local positioning system |
| Grantee: | Sergio Augusto Cunha |
| Support type: | Multi-user Equipment Program |
| FAPESP's process: | 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems |
| Grantee: | Leonor Patricia Cerdeira Morellato |
| Support type: | Research Program on Global Climate Change - University-Industry Cooperative Research (PITE) |
| FAPESP's process: | 15/24494-8 - Communications and processing of big data in cloud and fog computing |
| Grantee: | Nelson Luis Saldanha da Fonseca |
| Support type: | Research Projects - Thematic Grants |