(2016) Gotchas from mining bug reports.
In:
Perspectives on Data Science for Software Engineering.
Morgan Kaufmann, pp. 261-265.
Full text not available from this repository.
Official URL: http://doi.org/10.1016/B978-0-12-804206-9.00047-7
Abstract
Over the years, it has become common practice in empirical software engineering to mine data from version archives and bug databases to learn where bugs have been fixed in the past, or to build prediction models to find error-prone code in the future. However, most of these approach rely on strong assumptions that need to be verified to ensure that resulting models are accurate and reflect the intended property which can have serious consequences for decisions based on such flawed models.
| Item Type: | Book Section |
|---|---|
| Depositing User: | Sascha Just |
| Date Deposited: | 15 Feb 2018 08:09 |
| Last Modified: | 18 Jul 2019 12:12 |
| Primary Research Area: | NRA5: Empirical & Behavioral Security |
| URI: | https://publications.cispa.saarland/id/eprint/1475 |
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