Search

Industrial embedded systems often require special-ized hardware. However, software engineers have access to such domain-specific hardware only at the continuous integration (CI) stage and have to use simulated hardware otherwise. This results in a higher proportion of compilation errors at the CI stage than in other types of systems, warranting a deeper study. To this end, we create a CI diagnostics solution called 'Shadow Job' that analyzes our industrial CI system. We collected over 40000 builds from 4 projects from the product source code and categorized the compilation errors into 14 error types, showing that the five most common ones comprise 89 % of all compilation errors. Additionally, we analyze the resolution time, size, and distance for each error type, to see if different types of compilation errors are easier to localize or repair than others. Our results show that the resolution time, size, and distance are independent of each other. Our research also provides insights into the human effort required to fix the most common industrial compilation errors. We also identify the most promising directions for future research on fault localization.

Domain-Specific Hardware Accelerators (DSHA) are natural components in the evolution of general computers. However, designing and simulating hardware in Hardware Description Languages (HDL) often requires more effort for the developers and might not be suitable in all scenarios, which makes high-level language-based software simulators for computer hardware attractive. Yet, choosing which simulation framework to use can be challenging due to the lack of comparative studies of high-level language-based simulators. This paper presents a comparative evaluation of state-of-the-art simulation frameworks that simulate computer hardware in high-level languages like C++. The contemporary simulators used in this study were selected from the 79 articles introducing novel AI accelerators referenced in our previous survey. We have identified six simulators that are suitable for AI accelerator evaluation, and provide a deeper analysis of three of them. © 2024 IEEE.