Abstract
The rise of highly configurable complex software and its widespread usage requires design of efficient testing methodology. t-wise coverage is a leading metric to measure the quality of the testing suite and the underlying test generation engine. While uniform sampling-based test generation is widely believed to be the state of the art approach to achieve t-wise coverage in presence of constraints on the set of configurations, such a scheme often fails to achieve high t-wise coverage in presence of complex constraints. In this work, we propose a novel approach Baital, based on adaptive weighted sampling using literal weighted functions, to generate test sets with high t-wise coverage. We demonstrate that our approach reaches significantly higher t-wise coverage than uniform sampling. The novel usage of literal weighted sampling leaves open several interesting directions, empirical as well as theoretical, for future research.
Original language | English |
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Title of host publication | ESEC/FSE 2020 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering |
Editors | Prem Devanbu, Myra Cohen, Thomas Zimmermann |
Number of pages | 13 |
Publisher | Association for Computing Machinery |
Publication date | 8 Nov 2020 |
Pages | 1114-1126 |
ISBN (Electronic) | 9781450370431 |
DOIs | |
Publication status | Published - 8 Nov 2020 |
Event | 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2020 - Virtual, Online, United States Duration: 8 Nov 2020 → 13 Nov 2020 |
Conference
Conference | 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2020 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 08/11/2020 → 13/11/2020 |
Sponsor | ACM SIGSOFT |
Bibliographical note
Funding Information:This work was supported in part by EU H2020 project Serums (826278-SERUMS-H2020-SC1-FA-DTS-2018-2020) and by National Research Foundation Singapore under its NRF Fellowship Programme [NRF-NRFFAI1-2019-0004 ] and AI Singapore Programme [AISG-RP-2018-005], and NUS ODPRT Grant [R-252-000-685-13]. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.
Publisher Copyright:
© 2020 ACM.
Keywords
- Configurable software
- T-wise coverage
- Weighted sampling