Abstract
Large software projects such as Mozilla Firefox and Eclipse own more than ten thousand bug reports that have been reported but left unresolved. To utilize such a great amount of unresolved bug reports and accelerate bug detection and removal, we propose to a way recommend programmers a bug report that is likely to contain failure descriptions related to a source file being inspected. We employ the vector space model (VSM) to make a relevancy ranking of bug reports to a given source file. The result of an experiment using data of three open source software projects showed that the accuracies of recommendations ranged from 21.74% to 60.05% in terms of the percentage of recommendations that contained relevant bug reports in a top 10 recommended list.
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 9-12 |
| Number of pages | 4 |
| ISBN (Print) | 9781467369237 |
| DOIs | |
| Publication status | Published - Mar 30 2015 |
| Externally published | Yes |
| Event | 2015 1st IEEE International Workshop on Software Analytics, SWAN 2015 - Montreal, Canada Duration: Mar 2 2015 → … |
Other
| Other | 2015 1st IEEE International Workshop on Software Analytics, SWAN 2015 |
|---|---|
| Country | Canada |
| City | Montreal |
| Period | 3/2/15 → … |
Fingerprint
ASJC Scopus subject areas
- Software
- Information Systems
Cite this
Bug report recommendation for code inspection. / Fujiwara, Shin; Hata, Hideaki; Monden, Akito; Matsumoto, Kenichi.
2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 9-12 7070481.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Bug report recommendation for code inspection
AU - Fujiwara, Shin
AU - Hata, Hideaki
AU - Monden, Akito
AU - Matsumoto, Kenichi
PY - 2015/3/30
Y1 - 2015/3/30
N2 - Large software projects such as Mozilla Firefox and Eclipse own more than ten thousand bug reports that have been reported but left unresolved. To utilize such a great amount of unresolved bug reports and accelerate bug detection and removal, we propose to a way recommend programmers a bug report that is likely to contain failure descriptions related to a source file being inspected. We employ the vector space model (VSM) to make a relevancy ranking of bug reports to a given source file. The result of an experiment using data of three open source software projects showed that the accuracies of recommendations ranged from 21.74% to 60.05% in terms of the percentage of recommendations that contained relevant bug reports in a top 10 recommended list.
AB - Large software projects such as Mozilla Firefox and Eclipse own more than ten thousand bug reports that have been reported but left unresolved. To utilize such a great amount of unresolved bug reports and accelerate bug detection and removal, we propose to a way recommend programmers a bug report that is likely to contain failure descriptions related to a source file being inspected. We employ the vector space model (VSM) to make a relevancy ranking of bug reports to a given source file. The result of an experiment using data of three open source software projects showed that the accuracies of recommendations ranged from 21.74% to 60.05% in terms of the percentage of recommendations that contained relevant bug reports in a top 10 recommended list.
UR - http://www.scopus.com/inward/record.url?scp=84930470196&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84930470196&partnerID=8YFLogxK
U2 - 10.1109/SWAN.2015.7070481
DO - 10.1109/SWAN.2015.7070481
M3 - Conference contribution
AN - SCOPUS:84930470196
SN - 9781467369237
SP - 9
EP - 12
BT - 2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -