SHF: Medium: Principled Co-Reasoning of Software and Natural-Language Artifacts

SHF：媒介：软件和自然语言制品的原则性共同推理

• 批准号: 1901242
• 负责人: Lin Tan
• 金额: $ 90万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901242&HistoricalAwards=false
• 关键词: SHF; Medium; Principled; Co; Reasoning

Software is pervasive. Software typically contains a large volume of artifacts written in natural languages (NL), including code comments, change logs, manual pages, constant strings in code, and variable and function names. Software NL artifacts contain a wealth of semantic information that is often missing in code artifacts. There has been substantial existing work on analyzing NL artifacts and leveraging them in a wide range of software-engineering applications. However, most existing work is ad hoc, and is limited in its generality. Existing work typically considers NL artifacts as sources for additional information instead of first-class objects on which analysis operates (like variable types in program analysis), missing the opportunity to take full advantage of software NL artifacts. Thus, this project develops co-analysis of code and NL artifacts, which treats NL artifacts as first-class objects. In addition to advancing the state of the art, the principles, infrastructure, and techniques developed in the project are transformative, providing educational and practical tools to generate high-quality source code and software documents. These techniques improve program analysis, software maintenance, software reliability, and engineering productivity, for lower software development cost and better work and recreational lives, where software is indispensable.The project develops a principled and sophisticated software reasoning method that couples NL analysis and program analysis. It automatically models and classifies various kinds of NL artifacts, and attributes them to the related code elements. As such, they become first-class objects just like other classic objects in program analysis (e.g., variables and statements). They can be inferred, propagated, updated, associated, and formally reasoned about, to maximize the utilization of their rich semantics (e.g., comments can be propagated to code elements that are not previously commented through program analysis). The project activities include (1) modeling, classifying, and attributing NL artifacts, through developing domain-specific language models to process, model, classify NL artifacts and attribute them to the corresponding code elements, (2) building uniform representation, propagation, and co-reasoning of NL artifacts and code artifacts, (3) producing highly accurate and scalable probabilistic inference, by leveraging probabilistic graph models to perform the uniform reasoning of both code and NL artifacts, and (4) exploring new applications of co-analysis in domains including software testing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

软件无处不在。软件通常包含大量用自然语言（NL）编写的文物，包括代码注释，更改日志，手动页面，代码中的恒定字符串以及可变和功能名称。 软件NL工件包含大量的语义信息，这些信息通常在代码文物中缺少。 在分析NL工件并利用各种软件工程应用程序中，已经进行了大量现有工作。 但是，大多数现有的工作都是临时的，其一般性受到限制。 现有的工作通常将NL工件视为其他信息的来源，而不是分析所操作的一流对象（例如程序分析中的可变类型），缺少机会充分利用软件NL伪像。因此，该项目开发了代码和NL伪像的共分析，该分析将NL伪像是一流的对象。 除了推进最新技术外，项目中开发的原理，基础架构和技术具有变革性，提供了教育和实用的工具来生成高质量的源代码和软件文档。这些技术改善了计划分析，软件维护，软件可靠性和工程生产率，用于较低的软件开发成本，更好的工作和娱乐活动，在软件是必不可少的，该项目开发了一种原则上且复杂的软件推理方法，该方法将NL分析和程序分析伴侣。它会自动建模并分类各种NL工件，并将其归因于相关的代码元素。因此，它们就像程序分析中的其他经典对象一样（例如变量和语句），它们成为一流的对象。可以推断，传播，更新，关联和正式推理，以最大程度地利用其丰富的语义（例如，可以将评论传播到以前未通过程序分析以前未评论的代码元素）。项目活动包括（1）通过开发特定领域的语言模型来处理，模型，对NL伪像进行处理，将其归因于相应的代码元素，（2）通过（2）通过构建统一的代表，宣传和将NL伪像和代码定于（3）构建高度准确的范围（3）构建统一的代码元素（3）概率图模型以执行代码和NL工件的均匀推理，以及（4）探索包括软件测试在内的领域中共分析的新应用。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来通过评估来支持的。

项目成果

ABS: Scanning Neural Networks for Back-doors by Artificial Brain Stimulation

• DOI: 10.1145/3319535.3363216
• 发表时间: 2019-11
• 期刊: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security
• 作者: Yingqi Liu;Wen-Chuan Lee;Guanhong Tao;Shiqing Ma;Yousra Aafer;X. Zhang

How Eective Are Neural Networks for Fixing Security Vulnerabilities

神经网络修复安全漏洞的效果如何

• 发表时间: 2023
• 期刊: The ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA
• 作者: Wu, Yi;Jiang, Nan;Pham, Hung Viet;Lutellier, Thibaud;Davis, Jordan;Tan, Lin;Babkin, Petr;Shah, Sameena
• 通讯作者: Shah, Sameena

CPC: Automatically Classifying and Propagating Natural Language Comments via Program Analysis

• DOI: 10.1145/3377811.3380427
• 发表时间: 2020-06
• 期刊: 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE)
• 作者: Juan Zhai;Xiangzhe Xu;Yu Shi;Guanhong Tao;Minxue Pan;Shiqing Ma;Lei Xu;Weifeng Zhang;Lin Tan;X. Zhang

C2S: translating natural language comments to formal program specifications

• DOI: 10.1145/3368089.3409716
• 发表时间: 2020-11
• 期刊: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
• 作者: Juan Zhai;Yu Shi;Minxue Pan;Guian Zhou;Yongxiang Liu;Chunrong Fang;Shiqing Ma;Lin Tan;X. Zhang

Vicious Cycles in Distributed Software Systems

• DOI: 10.1109/ase56229.2023.00032
• 发表时间: 2023-09
• 期刊: 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)
• 作者: Shangshu Qian;Wen Fan;Lin Tan;Yongle Zhang