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202378 https://hdl.handle.net/2286/R.2.N.202378 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2025 41 pages Masters Thesis Academic theses en Soni, Vedant Wang, Fish Doupe, Adam Shoshitaishvili, Yan Arizona State University Partial requirement for: M.S., Arizona State University, 2025 Field of study: Computer Science Decompilation is the process of translating low-level, machine-executable code back into a high-level representation. Decompilers--tools that perform this translation which are essential for reverse engineers and security professionals, supporting critical tasks within their workflows. However, due to the loss of information during compilation-particularly as a result of optimizations, inlining, and other compiler-specific transformations-decompiled output is often incomplete or inaccurate. A central challenge in decompilation is accurate type inference: the reconstruction of high-level type information for variables based on low-level code patterns and memory access behaviors. Despite ongoing advancements in decompilation research, there is a notable lack of comprehensive comparative studies evaluating the type inference capabilities of existing decompilers. This thesis presents a benchmark study of five decompilers, focusing on their ability to infer types at both the function and variable levels. The evaluation is conducted on 94 real-world binaries, comprising a total of 7,540 functions. The results highlight the relative strengths and weaknesses of each decompiler and identify recurring scenarios in which incorrect type information is produced. Computer Science Cyber Security Decompilers Type Inference Benchmarking Type Inference in Decompilers