APISENSOR: Robust Discovery of Web API from Runtime Traffic Logs
Yanjing Yang, Chenxing Zhong, Ke Han, Zeru Cheng, Jinwei Xu, Xin Zhou, He Zhang, Bohan Liu

TL;DR
APISENSOR is a robust black-box framework that accurately discovers web APIs from complex runtime traffic logs, effectively handling mixed traffic noise and outperforming existing methods.
Contribution
It introduces a novel unsupervised API discovery method combining denoising, normalization, and graph-based clustering to improve robustness in complex environments.
Findings
Achieves over 95% group accuracy precision and 95% F1-score.
Outperforms state-of-the-art methods in accuracy and robustness.
Revealed real-world API documentation inconsistencies.
Abstract
Large Language Model (LLM)-based agents increasingly rely on APIs to operate complex web applications, but rapid evolution often leads to incomplete or inconsistent API documentation. Existing work falls into two categories: (1) static, white-box approaches based on source code or formal specifications, and (2) dynamic, black-box approaches that infer APIs from runtime traffic. Static approaches rely on internal artifacts, which are typically unavailable for closed-source systems, and often over-approximate API usage, resulting in high false-positive rates. Although dynamic black-box API discovery applies broadly, its robustness degrades in complex environments where shared collection points aggregate traffic from multiple applications. To improve robustness under mixed runtime traffic, we propose APISENSOR, a black-box API discovery framework that reconstructs application APIs…
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Taxonomy
TopicsSoftware System Performance and Reliability · Software Engineering Research · Service-Oriented Architecture and Web Services
