An experimental determination of sufficient mutant operators
ACM Transactions on Software Engineering and Methodology (TOSEM)
DAC '98 Proceedings of the 35th annual Design Automation Conference
POPL '80 Proceedings of the 7th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Proteum/IM 2.0: An Integrated Mutation Testing Environment
Mutation testing for the new century
Leveraging a Commercial Mutation Analysis Tool For Research
TAICPART-MUTATION '07 Proceedings of the Testing: Academic and Industrial Conference Practice and Research Techniques - MUTATION
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
A mutation model for the SystemC TLM 2.0 communication interfaces
Proceedings of the conference on Design, automation and test in Europe
High-Level Synthesis: Past, Present, and Future
IEEE Design & Test
XEMU: an efficient QEMU based binary mutation testing framework for embedded software
Proceedings of the tenth ACM international conference on Embedded software
On the Reuse of TLM Mutation Analysis at RTL
Journal of Electronic Testing: Theory and Applications
Word level feature discovery to enhance quality of assertion mining
Proceedings of the International Conference on Computer-Aided Design
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As high-level models in C and SystemC are increasingly used for verification and even design (through high-level synthesis) of electronic systems, there is a growing need for compatible error injection tools to facilitate further development of coverage metrics and automated diagnosis. This paper introduces SCEMIT, a tool for the automated injection of errors into C/C++/SystemC models. A selection of 'mutation' style errors are supported, and injection is performed though a plugin interface in the GCC compiler, which minimizes the impact of SCEMIT on existing simulation flows. Experimental injected error detection results are presented for the set of OSCI SystemC Example Models as well as the CHStone C High-Level-Synthesis benchmark set. Aside from demonstrating compatibility with these models, the results show the value of high-level error injection as a coverage measure compared to conventional code coverage measures.