Fault localization is a technique that plays a valuable role in reducing the burdensome task of hardware design code, such as Verilog or VHDL. The recent advancements in deep learning have demonstrated significant progress, showcasing the promising potential of various neural network architectures in comprehending and extracting meaningful insights from data. Importantly, this potential offers a new perspective that could potentially benefit fault localization techniques. In this article, we proposed Carpel: a context aware deep learning-based fault localization method for hardware design code. First, a dynamic execution coverage matrix and an error vector are generated by simulating the hardware code with various stimuli. Subsequently, a failing context is established to narrow the search space for fault localization. Next, three customized deep neural network models are developed and trained using the context-aware coverage matrix and error vector. Finally, a virtual test set is employed to these models to assess the suspiciousness of each statement. Experimental results indicate that our method outperforms state-of-the-art dynamic fault localization techniques, namely, Detraque, Tarsel, and Cirfix. Specifically, our approach identifies 37.03%, 22.22%, and 33.33% more faults than Detraque, Tarsel, and Cirfix, respectively, using the top-1 metric.

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.

The Internet of Things (IoT) has become more popular over the past decade. For the IoT to be successful, it is vital to track the location of these things (sensors or actuators). In this article, based on a new IoT underlying architecture, a narrowband-IoT (NB-IoT)-aided, bounding-based iterative and range-free method, named BBIL, is proposed to localize things. We make use of the location information of all anchor regular nodes that can help improve the localization accuracy as much as possible. Specifically, not only single-hop and multihop anchor things but also single-hop and multihop regular things are used for localization. In addition, the communication and computational loads of the local network are greatly decreased because the anchor things can directly access the Internet using the NB-IoT modules; hence, data can be sent to the Internet through a small number of hops in the local network and processed in the cloud/edge computing facilities in a centralized way. To balance the location accuracy and energy consumption of BBIL, we propose a theoretical model to obtain the optimal number of the anchor things. BBIL is evaluated and compared with the existing methods. The simulation results indicate that the average localization error of BBIL is less than 11.6%. Also, it performs well in anisotropic networks. In addition, we verified the validity of our method in real-world scenario.