Druyts, Sarah (2023) Exploring Design Decisions in Issue Tracking Systems for Projects in Different Software Domains. Bachelor's Thesis, Computing Science.
|
Text
Version_8_Final.pdf Download (3MB) | Preview |
|
![[img]](https://fse.studenttheses.ub.rug.nl/style/images/fileicons/text.png) |
Text
toestemming.pdf Restricted to Registered users only Download (132kB) |
Abstract
Software architecture is a topic of importance for any project which comprises more than two code files. However, formal documentation is often forgotten or deliberately neglected by developers, even though they might have discussed the design elsewhere, such as in an issue tracking system. Afterwards, this informal representation of the design knowledge may be difficult to rediscover. This thesis investigated potential links between Jira issue characteristics and the issue’s design decision content. First, a web GUI for a machine learning (ML) tool (12) was functionally extended to better facilitate manually labeling issues, in order to increase the training dataset for the ML models more easily. Next, using labels predicted by this machine learning tool, statistics were calculated on the patterns between software domains, issue labels, and issue characteristics. Finally, these statistical results were analysed and used to develop heuristics to find issues of certain decision types with higher frequency.
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Supervisor name: | Avgeriou, P. |
| Degree programme: | Computing Science |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 03 Jan 2024 09:32 |
| Last Modified: | 08 Jan 2024 10:29 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/31767 |
Actions (login required)
 |
View Item |