Using artificial bee colony for code coverage based test suite prioritization
© 2015 IEEE. The goal of test suite prioritization is maximizing fault detection and code coverage rate. Several nature inspired optimization algorithms such as Swarm Intelligence (SI) have been studied for the optimization of such problems. The studies revealed the benefits of Artificial Bee Colony...
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| Main Authors: | , |
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| Format: | Conference Proceeding |
| Published: |
2017
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| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84964680319&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/42162 |
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| Institution: | Chiang Mai University |
| Summary: | © 2015 IEEE. The goal of test suite prioritization is maximizing fault detection and code coverage rate. Several nature inspired optimization algorithms such as Swarm Intelligence (SI) have been studied for the optimization of such problems. The studies revealed the benefits of Artificial Bee Colony (ABC) over other algorithms. ABC and its variations were implemented in software testing areas, test suite prioritization in particular. However, most SI based approaches focus on fault detection ability which is difficult to predict. In this paper, the standard ABC algorithm is used to prioritize test suites based on code coverage. The results reveal that ABC shows promising results and, hence, is a great candidate for prioritizing test suites. It also suggests that a modification to the standard ABC algorithm or combination of ABC and another SI algorithm should yield an even better result. |
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