STRIDER: Signal Value Transition-Guided Defect Repair for HDL Programming Assignments

Hardware description languages (HDLs) are pivotal for the development of hardware designs. The programming courses for HDLs are also popular in both universities and online course platforms. Similar to programming assignments of software languages (SLs), these of HDLs also actively call for automated program repair (APR) techniques to provide personalized feedback for students. However, the research of APR techniques targeting HDL programming assignments is still in an early stage. Due to the significantly different programming mechanism of HDLs from SLs, the only APR technique (i.e., CirFix) targeting HDL programming assignments contributes a customized repair pipeline. However, the fundamental challenges in the design of HDL-oriented fault localization and patch generation still remain unresolved. In this work, we propose a signal value transition-guided defect repair technique named STRIDER by capturing the intrinsic features of HDLs. This technique consists of a time-aware dynamic defect localization approach to precisely localize defects, and a signal value transition-guided patch synthesis approach to effectively generate fixes.We further construct a dataset of 57 real defects from HDL programming assignments for tool evaluation. The evaluation reveals the overfitting issue of the pioneering tool CirFix and the significant improvement of STRIDER over CirFix in terms of both effectiveness and efficiency. In particular, STRIDER is more effective by correctly fixing 2.3X as many defects as CirFix in the real defect dataset, and is 23X more efficient by generating a correct fix within five minutes on average in the synthetic defect dataset, while CirFix takes around two hours on average.