一句话：提出首个辅助人工的 fuzz blocker 分析工具，用统一代码数据库与参数化查询帮助开发者定位覆盖率瓶颈根因。
模糊测试是发现软件漏洞的有效自动化测试方法，但许多 fuzzer 仍会因 fuzz blocker 陷入覆盖率平台期，从而难以发现更深层漏洞。本文提出 InsightQL，这是首个面向 fuzz blocker 分析的人工辅助工具。InsightQL 由统一代码数据库、直观的参数化查询界面和 VS Code 插件组成，使开发者能够推理 fuzz blocker 的根本原因，通常只需在插件中右键即可完成操作。该框架具有可扩展性，可支持 OSS-Fuzz 中的 C/C++ 项目，并可通过自定义查询进行扩展。摘要未报告定量评估结果，但提供了工具 screencast 演示。
Fuzzing is a highly effective automated testing method for uncovering software vulnerabilities. Despite advances in fuzzing techniques, many fuzzers still struggle with coverage plateaus caused by fuzz blockers, which limit their ability to discover deeper vulnerabilities. We introduce InsightQL, the first human-assisted tool for fuzz blocker analysis. InsightQL is powered by a unified database, an intuitive parameterised query interface, and a VS Code plugin. It enables developers to reason about the root causes of fuzz blockers, often with a simple right-click in the plugin. The framework is extensible and it could support C/C++ projects in OSS-Fuzz. It can also be easily extended with custom queries. A screencast of the tool is available at https://youtu.be/mFxYhmikXs8.
@inproceedings{paperbot12481,
title = {InsightQL: A Unified Code Database for Fuzz Blocker Analysis},
author = {Wentao Gao and Renata Borovica-Gajic and Sang Kil Cha and Michael Fu and Tian Qiu and Van-Thuan Pham},
booktitle = {ASE},
year = {2026},
doi = {10.1145/3832783.3834633}
}