Early Identification of Invalid Bug Reports in Industrial Settings – A Case Study
Laiq, Muhammad ; Ali, Nauman bin ; Böstler, Jürgen and Engström, Emelie LU
(2022)
23rd International Conference on Product-Focused Software Process Improvement, PROFES 2022
In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13709 LNCS.
p.497-507
- Abstract
Software development companies spend considerable time resolving bug reports. However, bug reports might be invalid, i.e., not point to a valid flaw. Expensive resources and time might be expended on invalid bug reports before discovering that they are invalid. In this case study, we explore the impact of invalid bug reports and develop and assess the use of machine learning (ML) to indicate whether a bug report is likely invalid. We found that about 15% of bug reports at the case company are invalid, and that their resolution time is similar to valid bug reports. Among the ML-based techniques we used, logistic regression and SVM show promising results. In the feedback, practitioners indicated an interest in using the tool to identify... (More)
Software development companies spend considerable time resolving bug reports. However, bug reports might be invalid, i.e., not point to a valid flaw. Expensive resources and time might be expended on invalid bug reports before discovering that they are invalid. In this case study, we explore the impact of invalid bug reports and develop and assess the use of machine learning (ML) to indicate whether a bug report is likely invalid. We found that about 15% of bug reports at the case company are invalid, and that their resolution time is similar to valid bug reports. Among the ML-based techniques we used, logistic regression and SVM show promising results. In the feedback, practitioners indicated an interest in using the tool to identify invalid bug reports at early stages. However, they emphasized the need to improve the explainability of ML-based recommendations and to reduce the maintenance cost of the tool.
(Less)
- author
- Laiq, Muhammad
; Ali, Nauman bin
; Böstler, Jürgen
and Engström, Emelie
LU
- organization
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Bug classification, Bug reports, Invalid bugs, Machine learning, Software analytics, Valid bugs
- host publication
- Product-Focused Software Process Improvement - 23rd International Conference, PROFES 2022, Proceedings
- series title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- editor
- Taibi, Davide ; Kuhrmann, Marco ; Mikkonen, Tommi ; Abrahamsson, Pekka and Klünder, Jil
- volume
- 13709 LNCS
- pages
- 11 pages
- publisher
- Springer Science and Business Media B.V.
- conference name
- 23rd International Conference on Product-Focused Software Process Improvement, PROFES 2022
- conference location
- Jyväskylä, Finland
- conference dates
- 2022-11-21 - 2022-11-23
- external identifiers
-
- scopus:85142737900
- ISSN
- 0302-9743
- 1611-3349
- ISBN
- 9783031213878
- DOI
- 10.1007/978-3-031-21388-5_34
- language
- English
- LU publication?
- yes
- id
- baeb2b3f-98d6-4314-b944-520129edbf8a
- date added to LUP
- 2022-12-29 13:29:07
- date last changed
- 2024-06-13 22:34:59
@inproceedings{baeb2b3f-98d6-4314-b944-520129edbf8a,
abstract = {{<p>Software development companies spend considerable time resolving bug reports. However, bug reports might be invalid, i.e., not point to a valid flaw. Expensive resources and time might be expended on invalid bug reports before discovering that they are invalid. In this case study, we explore the impact of invalid bug reports and develop and assess the use of machine learning (ML) to indicate whether a bug report is likely invalid. We found that about 15% of bug reports at the case company are invalid, and that their resolution time is similar to valid bug reports. Among the ML-based techniques we used, logistic regression and SVM show promising results. In the feedback, practitioners indicated an interest in using the tool to identify invalid bug reports at early stages. However, they emphasized the need to improve the explainability of ML-based recommendations and to reduce the maintenance cost of the tool.</p>}},
author = {{Laiq, Muhammad and Ali, Nauman bin and Böstler, Jürgen and Engström, Emelie}},
booktitle = {{Product-Focused Software Process Improvement - 23rd International Conference, PROFES 2022, Proceedings}},
editor = {{Taibi, Davide and Kuhrmann, Marco and Mikkonen, Tommi and Abrahamsson, Pekka and Klünder, Jil}},
isbn = {{9783031213878}},
issn = {{0302-9743}},
keywords = {{Bug classification; Bug reports; Invalid bugs; Machine learning; Software analytics; Valid bugs}},
language = {{eng}},
pages = {{497--507}},
publisher = {{Springer Science and Business Media B.V.}},
series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
title = {{Early Identification of Invalid Bug Reports in Industrial Settings – A Case Study}},
url = {{http://dx.doi.org/10.1007/978-3-031-21388-5_34}},
doi = {{10.1007/978-3-031-21388-5_34}},
volume = {{13709 LNCS}},
year = {{2022}},
}