APISENSOR: Robust Discovery of Web API from Runtime Traffic Logs

• URL: http://arxiv.org/abs/2603.23852v1
• Date: Wed, 25 Mar 2026 02:30:45 GMT
• Title: APISENSOR: Robust Discovery of Web API from Runtime Traffic Logs
• Abstract summary: APISENSOR is a black-box API discovery framework that reconstructs application APIs unsupervised.<n>We evaluated APISENSOR across six web applications using over 10,000 runtime requests with simulated mixed-traffic noise.<n>Results demonstrate that APISENSOR significantly improves discovery accuracy, achieving an average Group Accuracy Precision of 95.92% and an F1-score of 94.91%.
• Score: 10.620853813098503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 unsupervised. APISENSOR performs structured analysis over complex traffic, combining traffic denoising and normalization with a graph-based two-stage clustering process to recover accurate APIs. We evaluated APISENSOR across six web applications using over 10,000 runtime requests with simulated mixed-traffic noise. Results demonstrate that APISENSOR significantly improves discovery accuracy, achieving an average Group Accuracy Precision of 95.92% and an F1-score of 94.91%, outperforming state-of-the-art methods. Across different applications and noise settings, APISENSOR achieves the lowest performance variance and at most an 8.11-point FGA drop, demonstrating the best robustness among 10 baselines. Ablation studies confirm that each component is essential. Furthermore, APISENSOR revealed API documentation inconsistencies in a real application, later confirmed by community developers.

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