Context-aware Data Augmentation for Hardware Code Fault localization

The maintenance of quality and reliability in hardware products inherently relies on the verification of hardware code. Despite being a time-consuming process, the localization of faults in hardware code is essential for effective hardware verification. Dynamic fault localization (DFL) is considered one of the most effective methods for localizing faults. It utilizes coverage information, known as the coverage matrix, which is obtained from both passing and failing tests, to identify the program elements that are most likely responsible for observed failures in hardware programming languages such as VHDL or Verilog. However, the presence of error propagation, numerous fault-irrelevant statements, and highly imbalanced coverage information pose significant challenges for DFL. In this article, we propose Canal, a C ontext- a ware data augme n tation a pproach for dynamic fau l t localization in hardware code, to address these challenges. Canal effectively overcomes these challenges by leveraging in-time detection to prevent error propagation and utilizing program slicing to construct a semantic context that filters out fault-irrelevant statements. Additionally, it employs over-sampling techniques to balance the coverage information of passing and failing tests. Finally, the balanced coverage matrix is fed into DFL to compute the suspiciousness value of each statement. To evaluate the effectiveness of Canal, we conduct large-scale experiments on 10 programs and compare our method with 10 dynamic fault localizations. The experimental results clearly demonstrate that Canal surpasses the performance of all the compared DFL methods; e.g., Canal achieves an average improvement of 148.2% in Top-1 accuracy compared to the evaluation formulas.