Fuzzing is an essential element of software supply chain security governance. Despite its importance, the widespread adoption of library fuzzing is limited by the significant costs associated with constructing fuzz drivers. Without a clear entry point, the reachable path space of the target library is determined by the interplay of API call sequences, parameter dependencies, and state constraints. As a result, fuzz drivers must achieve not only successful builds but also provide sufficient semantic context to enable exploration of deeper state machine interactions, thereby avoiding premature stagnation at superficial validation logic. To systematically assess advancements in automated fuzz driver generation, this paper develops a taxonomy organized around the primary data sources used to derive driver-generation constraints, categorizing existing approaches into four technological trajectories: Usage Artifact Mining, Source Code Constraint Inference, Binary Semantics Recovery, and Heterogeneous Data Fusion. Large language models are increasingly integrated into these workflows as generators and as components for constraint alignment and repair. To address inconsistencies in experimental methodologies, this paper introduces a bounded comparability-oriented evaluation perspective focused on three dimensions: validity, reachability-related evidence, and reproducibility and cost. Together with a disclosure and reporting protocol for metric comparability, this perspective clarifies the information needed for cross-study comparison and examines the unique features and inherent limitations of each technical trajectory. Based on these findings, three key directions for future research are identified: facilitating structural evolution in response to coverage plateaus to address deep logic unreachability; coordinating dynamic closed-loop orchestration that utilizes on-demand heterogeneous data retrieval to resolve context challenges; and developing language-agnostic driver representations with pluggable adaptation mechanisms to improve cross-ecosystem portability and scalability.

Fuzzing is increasingly used to test ROS-based robotic systems, yet results remain difficult to compare because studies exercise different ROS layers and define failures in different ways. We analyze 92 selected works on ROS/ROS-based fuzzing and closely related robotic, cyber-physical, and autonomous-system testing. The corpus was built through a documented search, screening, and snowballing process. Each work is interpreted by its role in the fuzzing workflow and by the strength of its ROS-specific evidence, which separates direct ROS results from near-domain evidence that can be transferred only under explicit assumptions. Thereview shows a shift from message-validity testing toward runtime behavior and task-level failures, but the evidence profile is uneven: input modeling, feedback, and oracle construction have advanced more clearly than repeatable execution and diagnosable failure analysis. Treating ROS fuzzing as an evidence-bound workflow clarifies which claims can be compared, where evidence remains weak, and what support is needed for reproducible and repairable ROS fuzzing results.