Abstract
Search-based test data generation approaches have come a long way over the past few years, but these approaches still have some limitations when it comes to exercising specific behavior for triggering particular kinds of faults (e.g., crashes or specific types of integration between classes/modules). In this thesis, we are investigating new fitness functions and evolutionary-based algorithms and techniques to tackle these limitations. We have defined multiple novel approaches for crash reproduction and class integration testing. Currently, we are still working on improving both crash reproduction and class integration testing.
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) |
| Subtitle of host publication | Proceedings |
| Editors | E. O'Conner |
| Place of Publication | Piscataway |
| Publisher | IEEE |
| Pages | 424-426 |
| Number of pages | 3 |
| ISBN (Electronic) | 978-1-7281-5778-8 |
| ISBN (Print) | 978-1-7281-5779-5 |
| DOIs | |
| Publication status | Published - 2020 |
| Event | 13th IEEE International Conference on Software Testing, Verification and Validation, ICST 2020 - Porto, Portugal Duration: 23 Mar 2020 → 27 Mar 2020 |
Conference
| Conference | 13th IEEE International Conference on Software Testing, Verification and Validation, ICST 2020 |
|---|---|
| Country | Portugal |
| City | Porto |
| Period | 23/03/20 → 27/03/20 |
Keywords
- automated crash reproduction
- automated integration testing
- search-based software testing
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Cite this
Derakhshanfar, P. (2020). Well-informed Test Case Generation and Crash Reproduction. In E. O'Conner (Ed.), 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST): Proceedings (pp. 424-426). [9159102] IEEE. https://doi.org/10.1109/ICST46399.2020.00054