SHF: Small: Idiomatic Decompilation.

被称为逆向工程师的专家专门研究可执行程序，这些程序是没有源代码的计算机软件程序，表示为计算机上的低级机器指令。这个任务是困难的，因为将程序翻译到机器级别的过程丢弃了高级编程语言（如Java或C）提供的所有信息，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，以增强和转换非直观的可执行程序为更地道和可理解的软件代码。这项工作将帮助专业的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，并修复遗留软件系统中的错误。逆向工程师专门从事阅读和理解程序的行为，从其可执行文件中分析恶意软件，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、用户定义的数据库和惯用结构。 最先进的反编译工具生成的代码大多不符合习惯用法，即使是专家也很难理解。 该项目开发的技术将联合收割机从程序分析和机器学习中获得的洞察力结合起来，构建模型，将非直观的编译代码自动转换为更惯用和更易于理解的代码。特别地，这些模型（A）提供具有更多信息名称的通用变量标识符。(B)重新构造用户定义类型的名称和结构。(C)将非直观的控制流转换为更惯用的编程模式。该项目推进了两种语言模型的最新技术水平（需要新颖性，例如，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。

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.