Abstract
More bugs have been generated because today's software has become large and complex. Some of these bugs are critical, while others are trivial. Because accurate prediction of bug severity enables software developers to effectively solve software problems, that accuracy aids software development and project planning. Therefore, this study presents a reliable approach to improve predictions about bug severity by utilizing bugs classified as normal, which is the default level specified in a submitted report. This approach uses attributes as well as text information in bug reports to produce more accurate prediction results. Bug reports in open source projects such as Mozilla and Eclipse were used in the experiments. The result shows that this approach performs better than other studies.
| Original language | English |
|---|---|
| Pages (from-to) | 933-942 |
| Number of pages | 10 |
| Journal | Contemporary Engineering Sciences |
| Volume | 9 |
| Issue number | 19 |
| DOIs | |
| State | Published - 2016 |
Keywords
- Bug Severity Prediction
- Classification
- Machine Learning
- Normal Bugs
- Project Planning
- Software Development
