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.

几乎所有的现代生产软件都是经过优化编译的。可编程优化代码是软件开发过程的重要组成部分。应用程序开发人员可能不太了解的是，生产编译器确实包含错误，导致错误的调试信息。错误的调试信息会导致调试器崩溃或显示错误的变量值。这个项目将设计新颖，有效的方法，技术和工具，以验证在复杂的，现实世界的优化编译器的调试信息生成。该项目的新颖之处在于（1）提供了对验证调试信息以优化编译器的基本和实际挑战的更深入和更透彻的理解，以及（2）解决了调试信息验证的两个最基本的方面：测试程序生成和调试操作生成。该项目的影响是（1）提高开发人员调试优化代码和部署软件的能力，（2）更可靠和可用的调试器，从而间接提高软件质量。该项目的目标是探索新的，实用的技术，用于验证优化编译器生成的调试信息。它专注于开发一个系统框架，以提高与优化代码相关的调试信息的正确性。这个项目探索了三个主要方向：（1）开发新的策略来生成可操作的程序;（2）为可操作的程序发出全面的调试序列;（3）为不同的编程语言推广技术。该项目显著推进了调试信息测试和验证的最新技术水平。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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