The level of confidence in a VHDL description directly depends on the quality of its verification. This quality can be evaluated by mutation-based test, but the improvement of this quality requires tremendous efforts. In this paper, we propose a new approach that both qualifies and improves the functional verification process. First, we qualify test cases thanks to the mutation testing metrics: faults are injected in the design under verification (DUV) (making DUV's mutants) to check the capacity of test cases to detect theses mutants. Then, a heuristic is used to automatically improve IPs validation data. Experimental results obtained on RTL descriptions from ITC'99 benchmark show how efficient is our approach.

Software simulation remains the most used method for VHDL RTL functional verification. The functional verification process essentially consists of two parts. The first one is the functional qualification; the second one is the qualification- driven stimuli generation. Currently, the qualification and the generation tasks are iterative processes based on VHDL simulation which is dramatically time consuming. The simulation time increases with the circuits’ size and the required level of quality. In our previous works, we have proposed some approaches based on the mutation testing technique to evaluate and to improve functional validation quality. Now, to reduce this simulation time, we propose in this paper a new approach based on FPGA emulation. So, an hardware-software platform called “Meta-Mutant Testbench” is used to emulate mutants. Experimental results for some ITC’99 benchmark circuits show that our mutation emulator is about 20 times faster than classical software simulators; this speedup increases with the circuits’ size.

In current hardware design flow, functional verification is widely acknowledged as the crucial step. This paper presents a new contribution to reduce the cost of this step by automating it. We address here, one of the principal challenges of dynamic verification, by providing a new approach for automatic test generation. This approach combines mutation-based test techniques and genetic algorithms to produce stimuli for design under test. The feasibility of the proposed approach is assessed with a preliminary implementation, and some framework has been tested

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