With the rapid advancement of automated driving technology, numerous manufacturers deploy vehicles with auto-driving features. This highlights the importance of ensuring the quality of automated driving software. To achieve this, characterizing bugs in automated driving software is important, as it can facilitate bug detection and bug fixes, thereby ensuring software quality. Automated driving software typically has a modular architecture, where software is divided into multiple modules, each designed for its own functionality for automated driving. This may lead to varying bug characteristics. Additionally, our recent study has shown a correlation between bugs caused by code clones and the functionalities of modules in automated driving software. Hence, we consider the modular structure when analyzing bug characteristics. In this paper, we analyze 3,078 bugs from two representative open-source Level-4 automated driving systems, Apollo and Autoware. By analyzing the bug report description, title, and developers’ discussions, we have identified 20 bug symptoms and 17 bug-fixing strategies, and analyzed their relationships with the respective modules. Our analysis achieves 12 main findings offering a comprehensive view of bug characteristics in automated driving software. We believe our findings can help developers better understand and manage bugs in automated driving software, thereby improving software quality and reliability.

With the continuous improvement of artificial intelligence technology, autonomous driving technology has been greatly developed. Hence automated driving software has drawn more and more attention from both researchers and practitioners. Code clone is a commonly used to speed up the development cycle in software development, but many studies have shown that code clones may affect software maintainability. Currently, there is little research investigating code clones in automated driving software. To bridge this gap, we conduct a comprehensive experience study on the code clones in automated driving software. Through the analysis of Apollo and Autoware, we have presented that code clones are prevalent in automated driving software. about 30% of code lines are involved in code clones and more than 50% of files contain code clones. Moreover, a notable portion of these code clones has caused bugs and co-modifications. Due to the high complexity of autonomous driving, the automated driving software is often designed to be modular, with each module responsible for a single task. When considering each module individually, we have found that Perception, Planning, Canbus, and Sensing modules are more likely to encounter code clones, and more likely to have bug-prone and co-modified clones. Finally, we have shown that there exist cross-module clones to propagate bugs and co-modifications in different modules, which undermine the software's modularity.