Unmanned aerial vehicles (UAVs) are becoming increasingly ubiquitous in our daily lives. However, like many other complex systems, UAVs are susceptible to software bugs that can lead to abnormal system behaviors and undesirable consequences. It is crucial to study such software bug-induced UAV anomalies, which are often manifested in flight logs, to help assure the quality and safety of UAV systems. However, there has been limited research on investigating the code-level patterns of software bug-induced UAV anomalies. This impedes the development of effective tools for diagnosing and localizing bugs within UAV system code.

Unmanned aerial vehicles (UAVs) are becoming increasingly important and widely used in modern society. Software bugs in these systems can cause severe issues, such as system crashes, hangs, and undefined behaviors. Some bugs can also be exploited by hackers to launch security attacks, resulting in catastrophic consequences. Therefore, techniques that can help detect and fix software bugs in UAVs are highly desirable. However, although there are many existing studies on bugs in various types of software, the characteristics of UAV software bugs have never been systematically studied. This impedes the development of tools for assuring the dependability of UAVs. To bridge this gap, we conducted the first large-scale empirical study on two well-known open-source autopilot software platforms for UAVs, namely PX4 and Ardupilot, to characterize bugs in UAVs. Through analyzing 569 bugs from these two projects, we observed eight types of UAV-specific bugs (i.e., limit, math, inconsistency, priority, parameter, hardware support, correction, and initialization) and learned their root causes. Based on the bug taxonomy, we summarized common bug patterns and repairing strategies. We further identified five challenges associated with detecting and fixing such UAV-specific bugs. Our study can help researchers and practitioners to better understand the threats to the dependability of UAV systems and facilitate the future development of UAV bug diagnosis tools.

Extended Reality (XR) technologies are becoming increasingly popular in recent years. To help developers deploy XR applications on the Web, W3C released the WebXR Device API in 2019, which enable users to interact with browsers using XR devices. Given the convenience brought by WebXR, a growing number of WebXR projects have been deployed in practice. However, many WebXR applications are insufficiently tested before being released. They suffer from various bugs that can degrade user experience or cause undesirable consequences. Yet, the community has limited understanding towards the bugs in the WebXR ecosystem, which impedes the advance of techniques for assuring the reliability of WebXR applications. To bridge this gap, we conducted the first empirical study of WebXR bugs. We collected 368 real bugs from 33 WebXR projects hosted on GitHub. Via a seven-round manual analysis of these bugs, we built a taxonomy of WebXR bugs according to their symptoms and root causes. Furthermore, to understand the uniqueness of WebXR bugs, we compared them with bugs in conventional JavaScript programs and web applications. We believe that our findings can inspire future researches on relevant topics and we released our bug dataset to facilitate follow-up studies.