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Abstract
This paper proposes a supervised machine learning approach for predicting the root cause of a given bug report. Knowing the root cause of a bug can help developers in the debugging process—either directly or indirectly by choosing proper tool support for the debugging task. We mined 54755 closed bug reports from the issue trackers of 103 GitHub projects and applied a set of heuristics to create a benchmark consisting of 10459 reports. A subset was manually classified into three groups (semantic, memory, and concurrency) based on the bugs’ root causes. Since the types of root cause are not equally distributed, a combination of keyword search and random selection was applied. Our data set for the machine learning approach consists of 369 bug reports (122 concurrency, 121 memory, and 126 semantic bugs). The bug reports are used as input to a natural language processing algorithm. We evaluated the performance of several classifiers for predicting the root causes for the given bug reports. Linear Support Vector machines achieved the highest mean precision (0.74) and recall (0.72) scores. The created bug data set and classification are publicly available.
Original language | English |
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Title of host publication | Proceedings - 2020 IEEE 31st International Symposium on Software Reliability Engineering Workshops, ISSREW 2020 |
Editors | Marco Vieira, Henrique Madeira, Nuno Antunes, Zheng Zheng |
Publisher | IEEE Computer Society Conference Publishing Services |
Pages | 171-176 |
Number of pages | 6 |
ISBN (Electronic) | 9781728198705 |
DOIs | |
Publication status | Published - Oct 2020 |
Event | 31st International Symposium on Software Reliability Engineering: 2020 ISSRE - Virtual, Coimbra, Portugal Duration: 12 Oct 2020 → 12 Oct 2020 http://2020.issre.net/ |
Conference
Conference | 31st International Symposium on Software Reliability Engineering |
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Abbreviated title | ISSRE 2020 |
Country/Territory | Portugal |
City | Virtual, Coimbra |
Period | 12/10/20 → 12/10/20 |
Internet address |
Keywords
- bug report
- bug benchmark
- root cause prediction
ASJC Scopus subject areas
- Software
- Safety, Risk, Reliability and Quality
Fields of Expertise
- Information, Communication & Computing
Treatment code (Nähere Zuordnung)
- Basic - Fundamental (Grundlagenforschung)
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