Kim, J., You, B., Kwon, M. et al. (2 more authors) (2017) Evaluating CAVM: A New Search-Based Test Data Generation Tool for C. In: Menzies, T. and Petke, J., (eds.) SSBSE 2017: Search Based Software Engineering. International Symposium on Search-Based Software Engineering (SSBSE 2017), 09-11 Sep 2017, Paderborn, Germany. Lecture Notes in Computer Science . Springer Cham , pp. 143-149.
Abstract
We present CAVM (pronounced “ka-boom”), a new search-based test data generation tool for C. CAVM is developed to augment an existing commercial tool, CodeScroll, which uses static analysis and input partitioning to generate test data. Unlike the current state-of-the-art search-based test data generation tool for C, Austin, CAVM handles dynamic data structures using purely search-based techniques. We compare CAVM against CodeScroll and Austin using 49 C functions, ranging from small anti-pattern case studies to real world open source code and commercial code. The results show that CAVM can cover branches that neither CodeScroll nor Austin can, while also exclusively achieving the highest branch coverage for 20 of the studied functions.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © Springer International Publishing AG 2017. This is an author produced version of a paper subsequently published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Jun 2017 14:42 |
Last Modified: | 19 Dec 2022 13:36 |
Published Version: | https://doi.org/10.1007/978-3-319-66299-2_12 |
Status: | Published |
Publisher: | Springer Cham |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-66299-2_12 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:117808 |