Bug report recommendation for code inspection

Shin Fujiwara; Hideaki Hata; Akito Monden; Kenichi Matsumoto

doi:10.1109/SWAN.2015.7070481

Bug report recommendation for code inspection

Shin Fujiwara, Hideaki Hata, Akito Monden, Kenichi Matsumoto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

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 (Electronic)	9781467369237
DOIs	https://doi.org/10.1109/SWAN.2015.7070481
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 → …

Publication series

Name	2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings

Other

Other	2015 1st IEEE International Workshop on Software Analytics, SWAN 2015
Country/Territory	Canada
City	Montreal
Period	3/2/15 → …

ASJC Scopus subject areas

Software
Information Systems

Access to Document

10.1109/SWAN.2015.7070481

Cite this

Fujiwara, S., Hata, H., Monden, A., & Matsumoto, K. (2015). Bug report recommendation for code inspection. In 2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings (pp. 9-12). [7070481] (2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SWAN.2015.7070481

Bug report recommendation for code inspection. / Fujiwara, Shin; Hata, Hideaki; Monden, Akito et al.

2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 9-12 7070481 (2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fujiwara, S, Hata, H, Monden, A & Matsumoto, K 2015, Bug report recommendation for code inspection. in 2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings., 7070481, 2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 9-12, 2015 1st IEEE International Workshop on Software Analytics, SWAN 2015, Montreal, Canada, 3/2/15. https://doi.org/10.1109/SWAN.2015.7070481

Fujiwara S, Hata H, Monden A, Matsumoto K. Bug report recommendation for code inspection. In 2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 9-12. 7070481. (2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings). doi: 10.1109/SWAN.2015.7070481

@inproceedings{ca41e399ebf74bc980e8e33c8f6e94be,

title = "Bug report recommendation for code inspection",

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.",

author = "Shin Fujiwara and Hideaki Hata and Akito Monden and Kenichi Matsumoto",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 1st IEEE International Workshop on Software Analytics, SWAN 2015 ; Conference date: 02-03-2015",

year = "2015",

month = mar,

day = "30",

doi = "10.1109/SWAN.2015.7070481",

language = "English",

series = "2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "9--12",

booktitle = "2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings",

}

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

T3 - 2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings

SP - 9

EP - 12

BT - 2015 IEEE 1st International Workshop on Software Analytics, SWAN 2015 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2015 1st IEEE International Workshop on Software Analytics, SWAN 2015

Y2 - 2 March 2015

ER -

Bug report recommendation for code inspection

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this