Web Execution Bundles: Reproducible, Accurate, and Archivable Web Measurements

2025-01-27

AI Summary

Existing web measurement tools suffer from low accuracy, poor reproducibility, and weak evasion resistance, hindering result verification and replication. This paper introduces WebREC—the first general-purpose web measurement framework enabling high-fidelity event attribution—and proposes the executable, verifiable, and long-term archival .web binary archive format. WebREC achieves deep execution capture via browser automation and fine-grained event provenance, augmented by a lightweight Chromium instrumentation layer that jointly ensures measurement accuracy, broad applicability, and behavioral completeness. Experimental evaluation demonstrates: (1) a 62% reduction in measurement error when reproducing seminal studies; (2) direct support for 70% of measurement tasks from state-of-the-art SoK papers; and (3) offline analysis capability for 48% of tasks using archived .web files—eliminating the need for re-crawling. WebREC thus advances web measurement toward rigor, reproducibility, and sustainability.

Technology Category

• Data Mining & Knowledge Management: Knowledge Acquisition from the Web
• Application Domains: Web
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Web Mining and Content Analysis: Web measurements
• Responsible Web: Measurement, analysis, and circumvention of Web censorship
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Recently, reproducibility has become a cornerstone in the security and privacy research community, including artifact evaluations and even a new symposium topic. However, Web measurements lack tools that can be reused across many measurement tasks without modification, while being robust to circumvention, and accurate across the wide range of behaviors in the Web. As a result, most measurement studies use custom tools and varied archival formats, each of unknown correctness and significant limitations, systematically affecting the research's accuracy and reproducibility. To address these limitations, we present WebREC, a Web measurement tool that is, compared against the current state-of-the-art, accurate (i.e., correctly measures and attributes events not possible with existing tools), general (i.e., reusable without modification for a broad range of measurement tasks), and comprehensive (i.e., handling events from all relevant browser behaviors). We also present .web, an archival format for the accurate and reproducible measurement of a wide range of website behaviors. We empirically evaluate WebREC's accuracy by replicating well-known Web measurement studies and showing that WebREC's results more accurately match our baseline. We then assess if WebREC and .web succeed as general-purpose tools, which could be used to accomplish many Web measurement tasks without modification. We find that this is so: 70% of papers discussed in a 2024 web crawling SoK paper could be conducted using WebREC as is, and a larger number (48%) could be leveraged against .web archives without requiring any new crawling.

Problem

• Network Measurement
• Accuracy
• Reproducibility

Innovation

• WebREC
• Web Measurement
• Data Reproducibility