Enhancing Service Observability Through Bytecode-Level Variable Monitoring
Taizheng Wang, Chunyang Ye, Hui Xiang Zhou, Wei Chang, Chaoyi Li · 2025
Service-oriented architectures involve complex interactions, making failure detection and diagnosis challenging. Traditional static log statements often miss critical variables and lack runtime flexibility, resulting in limited observability. To address this, we propose a bytecode-level variable monitoring approach using the ASM library. Our tool transparently instruments service bytecode, enabling dynamic, source-free monitoring with minimal overhead. We further introduce a fusion model that analyzes bytecode semantics and structure to recommend critical variables at runtime. This enhances service observability and supports more efficient failure diagnosis.