Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-23352Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Panichella, Sebastiano | - |
| dc.contributor.author | Canfora, Gerardo | - |
| dc.contributor.author | Di Sorbo, Andrea | - |
| dc.date.accessioned | 2021-10-30T12:21:13Z | - |
| dc.date.available | 2021-10-30T12:21:13Z | - |
| dc.date.issued | 2021 | - |
| dc.identifier.issn | 0950-5849 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/23352 | - |
| dc.description.abstract | Context: Addressing user requests in the form of bug reports and Github issues represents a crucial task of any successful software project. However, user-submitted issue reports tend to widely differ in their quality, and developers spend a considerable amount of time handling them. Objective: By collecting a dataset of around 6,000 issues of 279 GitHub projects, we observe that developers take significant time (i.e., about five months, on average) before labeling an issue as a wontfix. For this reason, in this paper, we empirically investigate the nature of wontfix issues and methods to facilitate issue management process. Method: We first manually analyze a sample of 667 wontfix issues, extracted from heterogeneous projects, investigating the common reasons behind a “wontfix decision”, the main characteristics of wontfix issues and the potential factors that could be connected with the time to close them. Furthermore, we experiment with approaches enabling the prediction of wontfix issues by analyzing the titles and descriptions of reported issues when submitted. Results and conclusion: Our investigation sheds some light on the wontfix issues’ characteristics, as well as the potential factors that may affect the time required to make a “wontfix decision”. Our results also demonstrate that it is possible to perform prediction of wontfix issues with high average values of precision, recall, and F-measure (90%-93%). | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | Elsevier | de_CH |
| dc.relation.ispartof | Information and Software Technology | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject | Issue tracking | de_CH |
| dc.subject | Issue management | de_CH |
| dc.subject | Empirical study | de_CH |
| dc.subject | Machine learning | de_CH |
| dc.subject.ddc | 005: Computerprogrammierung, Programme und Daten | de_CH |
| dc.title | “Won’t we fix this issue?” : qualitative characterization and automated identification of wontfix issues on GitHub | de_CH |
| dc.type | Beitrag in wissenschaftlicher Zeitschrift | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Informatik (InIT) | de_CH |
| dc.identifier.doi | 10.1016/j.infsof.2021.106665 | de_CH |
| dc.identifier.doi | 10.21256/zhaw-23352 | - |
| zhaw.funding.eu | info:eu-repo/grantAgreement/EC/H2020/957254//DevOps for Complex Cyber-physical Systems/COSMOS | de_CH |
| zhaw.issue | 106665 | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | acceptedVersion | de_CH |
| zhaw.volume | 139 | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.webfeed | Software Systems | de_CH |
| zhaw.funding.zhaw | COSMOS – DevOps for Complex Cyber-physical Systems of Systems | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2021_Panichella-etal_Wontfix-issues-GitHub.pdf | Accepted Version | 1.04 MB | Adobe PDF |  View/Open |
Show simple item record
Panichella, S., Canfora, G., & Di Sorbo, A. (2021). “Won’t we fix this issue?” : qualitative characterization and automated identification of wontfix issues on GitHub. Information and Software Technology, 139(106665). https://doi.org/10.1016/j.infsof.2021.106665
Panichella, S., Canfora, G. and Di Sorbo, A. (2021) ‘“Won’t we fix this issue?” : qualitative characterization and automated identification of wontfix issues on GitHub’, Information and Software Technology, 139(106665). Available at: https://doi.org/10.1016/j.infsof.2021.106665.
S. Panichella, G. Canfora, and A. Di Sorbo, ““Won’t we fix this issue?” : qualitative characterization and automated identification of wontfix issues on GitHub,” Information and Software Technology, vol. 139, no. 106665, 2021, doi: 10.1016/j.infsof.2021.106665.
PANICHELLA, Sebastiano, Gerardo CANFORA und Andrea DI SORBO, 2021. “Won’t we fix this issue?” : qualitative characterization and automated identification of wontfix issues on GitHub. Information and Software Technology. 2021. Bd. 139, Nr. 106665. DOI 10.1016/j.infsof.2021.106665
Panichella, Sebastiano, Gerardo Canfora, and Andrea Di Sorbo. 2021. ““Won’t We Fix This Issue?” : Qualitative Characterization and Automated Identification of Wontfix Issues on GitHub.” Information and Software Technology 139 (106665). https://doi.org/10.1016/j.infsof.2021.106665.
Panichella, Sebastiano, et al. ““Won’t We Fix This Issue?” : Qualitative Characterization and Automated Identification of Wontfix Issues on GitHub.” Information and Software Technology, vol. 139, no. 106665, 2021, https://doi.org/10.1016/j.infsof.2021.106665.
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