FixRTL: Auto-correction of Multiple RTL Bugs by a New Feature Burst Clustering Algorithm and Mutation

Existing debugging and correction approaches suffer from weaknesses such as scalability, reproducing new bugs, and lacking a strategy to deal with multiple bugs. Hence, this article proposes FixRTL, a fully automated scalable methodology for localizing and correcting multiple bugs in Register-Transfer level (RTL) designs. FixRTL consists of three phases: (1) Constructing Samples , (2) Debugging , and (3) Correction . First, we simulate the design under verification (DUV), extract coverage data, and construct our samples. Since we are looking for buggy hit-statements, we use the proposed feature burst (FB) clustering algorithm in the Debugging Phase . The algorithm applies samples as train data, categorizes the encoded hit-statements into bursts, and uses them as test data to predict their cluster. Then we rank hit-statements based on their probability of containing bugs per cluster. In the Correction Phase , we apply a proposed mutation-based framework to correct high-ranked hit-statements. The results show that FixRTL reduces the percentage of hit-statements that must be examined to localize bugs on average by 44.3%. The results also demonstrate that FixRTL corrects 67% of injected bugs while recent existing works correct up to 25%. Moreover, unlike recent works, FixRTL offers corrections that match the grand truth.