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Type Inference on Executables.

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Název: Type Inference on Executables.
Autoři: CABALLERO, JUAN, ZHIQIANG LIN
Zdroj: ACM Computing Surveys; May2016, Vol. 48 Issue 4, p65:1-65:35, 35p, 3 Diagrams, 5 Charts
Témata: INFERENCE engines (Computer science), SOURCE code, DEBUGGING, BINARY codes, COMPUTER software execution, DATA types (Computer science), DATA structures
Abstrakt: In many applications, source code and debugging symbols of a target program are not available, and the only thing that we can access is the program executable. A fundamental challenge with executables is that, during compilation, critical information such as variables and types is lost. Given that typed variables provide fundamental semantics of a program, for the last 16 years, a large amount of research has been carried out on binary code type inference, a challenging task that aims to infer typed variables from executables (also referred to as binary code). In this article, we systematize the area of binary code type inference according to its most important dimensions: the applications that motivate its importance, the approaches used, the types that those approaches infer, the implementation of those approaches, and how the inference results are evaluated. We also discuss limitations, underdeveloped problems and open challenges, and propose further applications. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
Popis
Abstrakt:In many applications, source code and debugging symbols of a target program are not available, and the only thing that we can access is the program executable. A fundamental challenge with executables is that, during compilation, critical information such as variables and types is lost. Given that typed variables provide fundamental semantics of a program, for the last 16 years, a large amount of research has been carried out on binary code type inference, a challenging task that aims to infer typed variables from executables (also referred to as binary code). In this article, we systematize the area of binary code type inference according to its most important dimensions: the applications that motivate its importance, the approaches used, the types that those approaches infer, the implementation of those approaches, and how the inference results are evaluated. We also discuss limitations, underdeveloped problems and open challenges, and propose further applications. [ABSTRACT FROM AUTHOR]
ISSN:03600300
DOI:10.1145/2896499