SRepair: Symbolic Regression-Based Repair for Hardware Design Code

Fixing bugs in hardware design code has become a challenging task due to the increasing complexity of modern circuit designs. As a result, automated program repair techniques have been proposed to synthesize patches for bugs in hardware designs and achieved promising results. However, existing techniques are still limited in synthesizing expressions for complex bugs. In this work, we explore the possibility of addressing complex bugs by proposing SREPAIR, a novel symbolic regression-based repair technique. The key novelty of SREPAIR lies in three aspects: 1) we propose a novel expression modification encoding that enables fine-grained adjustments to buggy expressions. 2) we introduce expression synthesis-based templates that allow for flexible and expressive repairs. 3) we develop a novel symbolic regression network-based synthesis algorithm that effectively synthesizes complex expressions. Experimental results on the four peer-reviewed datasets demonstrate that SREPAIR correctly fixes 56 bugs out of 112 bugs, which achieves 43.6% and 194.7% improvement over the previous state-of-the-art RTL-REPAIR (39 bugs) and CIRFIX (19 bugs). To evaluate the generalizability of SREPAIR, we further construct an augmented dataset of 282 bugs by mutating hardware designs. SREPAIR shows its better generalizability by correctly fixing 127 bugs, reaching 217.5% improvement over the best approach.