Well-informed Test Case Generation and Crash Reproduction
Conference Paper
(2020)
Author(s)
Pouria Derakhshanfar (TU Delft - Software Engineering)
Research Group
Software Engineering
DOI related publication
https://doi.org/10.1109/ICST46399.2020.00054
Final published version
To reference this document use
https://resolver.tudelft.nl/uuid:7b8c4476-c698-46fe-8bc7-3d9b24d0d841
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Publication Year
2020
Language
English
Research Group
Software Engineering
Article number
9159102
Pages (from-to)
424-426
ISBN (print)
978-1-7281-5779-5
ISBN (electronic)
978-1-7281-5778-8
Event
13th IEEE International Conference on Software Testing, Verification and Validation, ICST 2020 (2020-03-23 - 2020-03-27), Porto, Portugal
Downloads counter
151
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.