一句话：提出并实现 AIRBORNE 工具，通过源码分析与克隆检测生成融合 Stack Overflow 引用信息的细粒度 SBOM。
现有软件物料清单（SBOM）生成工具粒度较粗，仅能捕获包级或库级依赖，而忽略了来自 Stack Overflow（SO）等问答论坛的代码片段复用。本文以工具演示形式提出 AIRBORNE，它分析软件项目并生成增强的细粒度 SBOM，其中纳入了开发者自认的 SO 引用信息。该工具集成源码分析、仓库挖掘与克隆检测技术，将 SO 来源与 CycloneDX SBOM 标准对齐。作者对 383 个包含已知自认 SO 引用的 GitHub 项目进行了初步评估，结果表明：当开发者通过自认引用显式承认复用了 SO 帖子中的代码片段时，AIRBORNE 能够捕获这些细粒度依赖。工具与演示视频均已公开。
SBOM (Software Bills of Materials) generation tools operate at coarse granularity, capturing only package- or library-level dependencies while overlooking smaller artifacts such as code snippets from Questions & Answers (Q&A) forums such as Stack Overflow (SO). In this tool demo paper, we present AIRBORNE (Augmented fIne-grained softwaRe Bill Of mateRials geNerator for stack ovErflow), a tool that analyzes a software project and generates an augmented fine-grained SBOM enriched with information on self-admitted SO references. AIRBORNE integrates source code analysis, repository mining, and clone detection techniques to align SO provenance with the CycloneDX SBOM standard. We conducted a preliminary assessment of 383 GitHub projects with known self-admitted SO references. The results show that our tool captures fine-grained dependencies whenever developers explicitly acknowledge code snippet reuse through self-admitted references to SO posts. Tool link: https://github.com/MSR4SBOM/airborne Video link: https://youtu.be/HX9wpnrM5tA
@inproceedings{paperbot12328,
title = {AIRBORNE: Augmented fIne-grained softwaRe Bill Of mateRials geNerator for stack ovErflow},
author = {Sabato Nocera and Massimiliano Di Penta and Simone Romano and Giuseppe Scanniello},
booktitle = {ASE},
year = {2026},
doi = {10.1145/3832783.3834631}
}