SHF:Small: Debug Information Validation for Optimizing Compilers

SHF:Small：优化编译器的调试信息验证

• 批准号: 2114627
• 负责人: Qirun Zhang
• 金额: $ 49.78万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114627&HistoricalAwards=false
• 关键词: SHF; Small; Debug; Information; Validation

Almost all modern production software is compiled with optimization. Debugging optimized code is an important part of the software-development process. Perhaps less known to application developers is that production compilers do contain bugs, leading to incorrect debug information. Wrong debug information causes debuggers to either crash or display wrong variable values. This project will devise novel, effective methodologies, techniques, and tools to validate the debug information generation in complex, real-world optimizing compilers. The project's novelties are (1) providing a deeper and more thorough understanding of the fundamental and practical challenges for validating debug information for optimizing compilers, and (2) addressing the two most fundamental aspects of debug-information validation: test-program generation and debug-action generation. The project's impacts are (1) increasing the capability of developers to debug optimized code and deployed software, and (2) more reliable and usable debuggers thereby indirectly improving the quality of software.The goal of this project is to explore novel, practical techniques for validating debug information generated by optimizing compilers. It focuses on developing a systematic framework to enhance the correctness of debug information associated with optimized code. This project explores three main directions: (1) developing new strategies to generate actionable programs; (2) emitting comprehensive debug sequences for actionable programs; and (3) generalizing techniques for different programming languages. The project significantly advances the state-of-the-art in debug information testing and validation.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.

几乎所有现代生产软件都是通过优化编制的。调试优化代码是软件开发过程的重要组成部分。应用程序开发人员也许鲜为人知的是，生产编译器确实包含错误，从而导致不正确的调试信息。错误的调试信息会导致调试者崩溃或显示错误的变量值。该项目将设计新颖，有效的方法，技术和工具，以验证复杂的现实世界优化编译器中的调试信息生成。该项目的新颖性是（1）对验证调试信息以优化编译器的基本和实用挑战提供了更深入，更透彻的了解，以及（2）解决调试信息验证的两个最基本方面：测试程序编程生成和DEADUG-ACTION生成。该项目的影响是（1）提高开发人员进行调试优化的代码和部署软件的能力，以及（2）更可靠且可用的调试器，从而间接提高软件的质量。该项目的目的是探索小说，实用的技术，以通过优化编译器来验证验证的调试信息。它着重于开发系统的框架，以增强与优化代码相关的调试信息的正确性。该项目探讨了三个主要方向：（1）制定新的策略来生成可行的程序； （2）为可行的程序发出综合调试序列； （3）针对不同编程语言的概括技术。该项目在调试信息测试和验证中大大提高了最先进的作用。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准，被认为值得通过评估来获得支持。

项目成果

Studying and Understanding the Tradeoffs Between Generality and Reduction in Software Debloating

• DOI: 10.1145/3551349.3556970
• 发表时间: 2022-10
• 期刊: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
• 作者: Qi Xin;Qirun Zhang;A. Orso

Recursive State Machine Guided Graph Folding for Context-Free Language Reachability

• DOI: 10.1145/3591233
• 发表时间: 2023-06
• 期刊: Proceedings of the ACM on Programming Languages
• 作者: Yuxiang Lei;Yulei Sui;Shin Hwei Tan;Qirun Zhang

Efficient algorithms for dynamic bidirected Dyck-reachability

• DOI: 10.1145/3498724
• 发表时间: 2022-01
• 期刊: Proceedings of the ACM on Programming Languages
• 作者: Yuanbo Li;K. Satya;Qirun Zhang

Static Inference Meets Deep learning: A Hybrid Type Inference Approach for Python

• DOI: 10.1145/3510003.3510038
• 发表时间: 2021-05
• 期刊: 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)
• 作者: Yun Peng;Cuiyun Gao;Zongjie Li;Bowei Gao;David Lo;Qi-reng Zhang;Michael R. Lyu