Automated issue assignment using topic modelling on Jira issue tracking data
Abstract As more and more software teams use online issue tracking systems to collaborate on software projects, the accurate assignment of new issues to the most suitable contributors may have significant impact on the success of the project. As a result, several research efforts have been directed...
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| Format: | Article |
| Language: | English |
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Wiley
2023-06-01
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| Series: | IET Software |
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| Online Access: | https://doi.org/10.1049/sfw2.12129 |
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| author | Themistoklis Diamantopoulos Nikolaos Saoulidis Andreas Symeonidis |
| author_facet | Themistoklis Diamantopoulos Nikolaos Saoulidis Andreas Symeonidis |
| author_sort | Themistoklis Diamantopoulos |
| collection | DOAJ |
| description | Abstract As more and more software teams use online issue tracking systems to collaborate on software projects, the accurate assignment of new issues to the most suitable contributors may have significant impact on the success of the project. As a result, several research efforts have been directed towards automating this process to save considerable time and effort. However, most approaches focus mainly on software bugs and employ models that do not sufficiently take into account the semantics and the non‐textual metadata of issues and/or produce models that may require manual tuning. A methodology that extracts both textual and non‐textual features from different types of issues is designed, providing a Jira dataset that involves not only bugs but also new features, issues related to documentation, patches, etc. Moreover, the semantics of issue text are effectively captured by employing a topic modelling technique that is optimised using the assignment result. Finally, this methodology aggregates probabilities from a set of individual models to provide the final assignment. Upon evaluating this approach in an automated issue assignment setting using a dataset of Jira issues, the authors conclude that it can be effective for automated issue assignment. |
| format | Article |
| id | doaj-art-24216f7d4ef1451ba9b205b49ee671c6 |
| institution | Kabale University |
| issn | 1751-8806 1751-8814 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Software |
| spelling | doaj-art-24216f7d4ef1451ba9b205b49ee671c62025-02-03T01:32:08ZengWileyIET Software1751-88061751-88142023-06-0117333334410.1049/sfw2.12129Automated issue assignment using topic modelling on Jira issue tracking dataThemistoklis Diamantopoulos0Nikolaos Saoulidis1Andreas Symeonidis2Electrical and Computer Engineering Department Aristotle University of Thessaloniki Thessaloniki GreeceElectrical and Computer Engineering Department Aristotle University of Thessaloniki Thessaloniki GreeceElectrical and Computer Engineering Department Aristotle University of Thessaloniki Thessaloniki GreeceAbstract As more and more software teams use online issue tracking systems to collaborate on software projects, the accurate assignment of new issues to the most suitable contributors may have significant impact on the success of the project. As a result, several research efforts have been directed towards automating this process to save considerable time and effort. However, most approaches focus mainly on software bugs and employ models that do not sufficiently take into account the semantics and the non‐textual metadata of issues and/or produce models that may require manual tuning. A methodology that extracts both textual and non‐textual features from different types of issues is designed, providing a Jira dataset that involves not only bugs but also new features, issues related to documentation, patches, etc. Moreover, the semantics of issue text are effectively captured by employing a topic modelling technique that is optimised using the assignment result. Finally, this methodology aggregates probabilities from a set of individual models to provide the final assignment. Upon evaluating this approach in an automated issue assignment setting using a dataset of Jira issues, the authors conclude that it can be effective for automated issue assignment.https://doi.org/10.1049/sfw2.12129software development managementsoftware engineeringsoftware maintenancesoftware management |
| spellingShingle | Themistoklis Diamantopoulos Nikolaos Saoulidis Andreas Symeonidis Automated issue assignment using topic modelling on Jira issue tracking data IET Software software development management software engineering software maintenance software management |
| title | Automated issue assignment using topic modelling on Jira issue tracking data |
| title_full | Automated issue assignment using topic modelling on Jira issue tracking data |
| title_fullStr | Automated issue assignment using topic modelling on Jira issue tracking data |
| title_full_unstemmed | Automated issue assignment using topic modelling on Jira issue tracking data |
| title_short | Automated issue assignment using topic modelling on Jira issue tracking data |
| title_sort | automated issue assignment using topic modelling on jira issue tracking data |
| topic | software development management software engineering software maintenance software management |
| url | https://doi.org/10.1049/sfw2.12129 |
| work_keys_str_mv | AT themistoklisdiamantopoulos automatedissueassignmentusingtopicmodellingonjiraissuetrackingdata AT nikolaossaoulidis automatedissueassignmentusingtopicmodellingonjiraissuetrackingdata AT andreassymeonidis automatedissueassignmentusingtopicmodellingonjiraissuetrackingdata |