CAREER: Enabling License Compliance Analysis and Verification for Evolving Software

该项目提出了一种新的统一模型，以帮助软件开发人员许可软件并（重新）使用符合法律要求的组件。该解决方案将研究信息检索、互联网规模的源代码搜索、存储库挖掘和静态分析方法的新组合，以检测软件组件的来源。该研究还将依靠反馈驱动的信息检索和机器学习技术的混合混合，以高精度地识别组件的许可证。此外，所建议的模型将统一这些用于许可证遵从性分析和验证的构建块，以推断给定的软件、组件、依赖项和许可证，以及它们的可信度、约束和现有或潜在的法律遵从性问题。所提出的研究将为复杂的法律遵从性问题的综合分析提供理论基础和实际解决方案，从而使合法的软件开发和演变成为可能。在更广泛的影响中，该项目将开发教育课程内容，涉及代表性不足的学生群体，并在开源许可下生产软件工具，与行业合作转让技术并对拟议的研究进行实证评估，并开展K-12外展活动。

This project proposes a novel unified model to help software developers license software and (re)use components complying with legal requirements. The solution will investigate novel combinations of information retrieval, internet-scale source code search, repository mining, and static analysis approaches to detect origins of software components. The research will also rely on a feedback-driven hybrid blending of information retrieval and machine learning techniques for identifying components' licenses with high accuracy. In addition, the proposed model will unify these building blocks for license compliance analysis and verification to reason about the given software, components, dependencies, and licenses, as well as their trustworthiness, constraints, and existing or potential legal compliance issues. The proposed research will lead to both theoretical foundations and practical solutions for the comprehensive analysis of complex legal compliance concerns to enable lawful software development and evolution. Among the broader impacts, the project will develop educational course content, involve underrepresented student groups, and produce software tools under open source licenses, collaborating with industry to transfer technology and empirically evaluate proposed research, and conducting K-12 outreach activities.