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Post-silicon validation has become essential in catching hard-todetect, rarely-occurring bugs that have slipped through pre-silicon verification. Post-silicon validation flows, however, are challenged by limited signal observability, which impacts their ability of diagnosing and detecting bugs. Indeed, bug manifestations during the execution of constrained-random tests may be masked and be unobservable from the test's outputs. The ability to evaluate the bug-masking rate of a test provides great value in generating and/or selecting effective tests for high coverage regressions. To this end, we propose an efficient, static bug-masking analysis solution, called BugMAPI. BugMAPI tracks the information flow in a test program, and it estimates the probability that bugs go undetected by the checking mechanisms in place in the post-silicon platform. To achieve this goal, we leverage static code analysis and a novel, lightweight, probability estimation algorithm. We evaluated BugMAPI on a range of industrial constrained-random tests and a range of bug injection models, and we found that it can estimate bugmasking rates with an accuracy of 77% in 3 orders-of-magnitude less time, compared to an ideal dynamic analysis solution.

Abstract-In post-silicon functional validation, one of the most complex and time-consuming processes is the localization of an instruction that exposes a bug detected at system level. The task is particularly difficult due to the silicon's limited observability and the long time between a failure's occurrence and its detection.We propose a novel method that automates the architectural localization of post-silicon test-case failures. Our proposed tool analyzes a failing test-case, while leveraging the information derived from executing the test on an Instruction Set software Simulator (ISS), to identify a set of instructions that could lead to the faulty final state. The proposed failure localization process comprises the creation of a resource dependency graph based on the execution of the test-case on the ISS, determining a program slice of instructions that influence the faulty resources, and the reduction of the set of suspicious instructions by leveraging the knowledge of the correct resources.We evaluate our proposed solution through extensive experiments. Experimental results show that, in over 97% of all cases, our method was able to narrow down the list of suspicious instructions to under 2 instructions, on average, out of over 200. In over 59% of all cases, our method correctly reduced a test-case to a single faulty instruction.

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