HDL-mutation based fault injection and analysis is considered as an important coverage metric for measuring the quality of design simulation processes [20, 3, 1, 2]. In this work, we try to solve the problem of automatic simulation data generation targeting HDL mutation faults. We follow a search based approach and eliminate the need for symbolic execution and mathematical constraint solving from existing work. An objective cost function is defined on the test input space and serves the guidance of search for fault-detecting test data. This is done by first mapping the simulation traces under a test onto a control and data flow graph structure which is extracted from the design. Then the progress of fault detection can be measured quantitatively on this graph to be the cost value. By minimizing this cost we approach the target test data. The effectiveness of the cost function is investigated under an example neighborhood search scheme. Case study with a floating point arithmetic IP design has shown that the cost function is able to guide effectively the search procedure towards a fault-detecting test. The cost calculation time as the search overhead was also observed to be minor compared to the actual design simulation time.

The topic will cover the use of functional qualification for measuring the quality of functional verification of TLM models. Functional qualification is based on the theory of mutation analysis but considers a mutation to have been killed only if a test case fails. A mutation model of TLM behaviors is proposed to qualify a verification environment based on both testcases and assertions. The presentation describes at first the theoretic aspects of this topic and then it focuses on its application to real cases by using actual EDA tools, thus showing advantages and limitations of the application of mutation analysis to TLM.

The results of the last MaxSAT Evaluations suggest there is no universal best algorithm for solving MaxSAT, as the fastest solver often depends on the type of instance. Having an oracle able to predict the most suitable MaxSAT solver for a given instance would result in the most robust solver. Inspired by the success of SATzilla for SAT, this paper describes the first approach for a portfolio of algorithms for MaxSAT. Compared to existing solvers, the resulting portfolio can achieve significant performance improvements on a representative set of instances.