SHF: Small: Idiomatic Decompilation.

被称为逆向工程师的专家专门研究可执行程序，即没有源代码的计算机软件程序，以计算机上的低级机器指令表示。这项任务很困难，因为将程序翻译到机器级别的过程会抛弃Java或C等高级编程语言提供的所有信息，而这些信息与帮助人类理解程序的功能有关。该项目开发分析和机器学习技术，将非直观的可执行程序扩展和转换为更习惯和可理解的软件代码。这项工作将帮助专业逆向工程师更有效地完成他们的工作；这些专业人员从事安全关键和经济关键的活动，如理解和响应恶意软件和病毒、发现漏洞和修复遗留软件系统中的错误。逆向工程师专注于从可执行文件中阅读和理解程序的行为，以分析恶意软件，发现软件漏洞，或修补遗留错误。不幸的是，编译器丢弃了大量对人类理解至关重要的信息：注释、名称、用户定义的数据类型和惯用结构。最先进的反编译工具生成的代码在很大程度上不符合习惯，即使是专家也很难理解。该项目开发的技术结合了程序分析和机器学习的洞察力来构建模型，以自动将非直观的编译代码转换为更习惯和可理解的代码。特别是，这些模型(A)提供具有更多信息名称的通用变量标识符。(B)重构用户定义类型的名称和结构。(C)将非直观的控制流程转换为更惯用的编程模式。该项目在语言模型（要求新颖，例如，神经语言模型和基于树的机器学习）和程序分析和转换方面推进了最先进的技术，以限制搜索问题。该奖项反映了美国国家科学基金会的法定使命，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。

Experts known as reverse engineers specialize in studying executable programs, which are computer software programs without source code, expressed as low-level machine instructions on a computer. This task is difficult because the process of translating a program to the machine level throws away all of the information provided by high level programming languages like Java or C that are relevant to helping humans understand what the program does. This project develops analysis and machine learning techniques to augment and transform non-intuitive executable programs into more idiomatic and understandable software code. This work will help professional reverse engineers do their jobs more effectively; these professionals engage in security-critical and economically-critical activities like understanding and responding to malware and viruses, discovering vulnerabilities, and fixing bugs in legacy software systems.Reverse engineers specialize in reading and understanding a program's behavior from its executable to analyze malware, discover software vulnerabilities, or patch legacy bugs. Unfortunately, compilers discard considerable information that is key to human understanding: comments, names, user-defined datatypes, and idiomatic structure. State-of-the-art de-compilation tools produce code that is largely not idiomatic, and can be very difficult for even experts to understand. This project develops techniques that combine insight from program analysis and machine learning to construct models to automatically transform non-intuitive compiled code into more idiomatic and understandable code. In particular, these models (A) Provide generic variable identifiers with more informative names. (B) Reconstruct names and structure of user-defined types. (C) Transform non-intuitive control flow into more idiomatic programming patterns. The project advances the state-of-the-art in both language models (requiring novelties in, e.g., neural language models and tree-based machine learning) and program analysis and transformation to constrain the search problems.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.