Disclosed are various embodiments for isolated code detection from application code analysis. Various application components may be identified from a source code file or a bytecode file of a computing application. A graph model representative of the computing application is generated having nodes and bridges that connect some nodes to other nodes. The graph model is generated such that at least one of the nodes is an isolated node having less than a threshold number of bridges connecting to other nodes, which is indicative that a corresponding one of the application components can be implemented as an independently deployable component of the computing application.

Disclosed are various embodiments for isolated code detection from application code analysis. Various application components may be identified from a source code file or a bytecode file of a computing application. A graph model representative of the computing application is generated having nodes and bridges that connect some nodes to other nodes. The graph model is generated such that at least one of the nodes is an isolated node having less than a threshold number of bridges connecting to other nodes, which is indicative that a corresponding one of the application components can be implemented as an independently deployable component of the computing application.

The disclosure invention provides a method for executing a program compiled for a source architecture on a machine having a different target architecture, a non-transitory computer readable medium configured to store instructions for performing such a method, and a system for performing such a method.

The disclosure invention provides a method for executing a program compiled for a source architecture on a machine having a different target architecture, a non-transitory computer readable medium configured to store instructions for performing such a method, and a system for performing such a method.

Systems, methods, and storage media for creating an interface between a smart contract to be executed on a decentralized architecture and a user component, the method comprising: receiving code corresponding to the smart contract at an interface server; the interface server parsing an application binary interface (ABI) corresponding to the smart contract; the interface server constructing an enhanced application binary interface (EABI) based on the ABI; and the interface server creating a REST API interface specific to the smart contract based on the EABI.

A method, system and product for determining a characterization of a terminal within a binary code, based on influences of the terminal. Based on the characterization of the terminal, the terminal is determined to be potentially affected by external input that is inputted to a device executing the binary code. A propagation path that indicates a reachability of the terminal within the binary code is determined. A code patch associated with a functionality of at least a portion of the binary code and with the propagation path of the terminal is located in the binary code. The code patch can be executed independently from the binary code. The code patch is extracted from the binary code for testing, and an emulation of the code patch is generated to enable fuzz testing of the emulation, whereby the code patch is tested independently.

A method, system and product for determining a characterization of a terminal within a binary code, based on influences of the terminal. Based on the characterization of the terminal, the terminal is determined to be potentially affected by external input that is inputted to a device executing the binary code. A propagation path that indicates a reachability of the terminal within the binary code is determined. A code patch associated with a functionality of at least a portion of the binary code and with the propagation path of the terminal is located in the binary code. The code patch can be executed independently from the binary code. The code patch is extracted from the binary code for testing, and an emulation of the code patch is generated to enable fuzz testing of the emulation, whereby the code patch is tested independently.

A recording medium stores a program for causing a computer to execute processing including: incrementing a counter every time translating a CISC instruction into a RISC instruction; updating previously referenced translation timing of a register to be used for translation with a value of the counter; in a case where a use register number that stores a register number to be used for translation of the memory operand is an initial value, selecting the register number, and updating the use register number to the selected register number; in a case where the use register number is not the initial value, and when the use register number is not used, skipping data restoration and data saving for the register of the use register number, and generating an instruction to read data of the memory operand by using the register; and generating the RISC instruction equivalent to the CISC instruction.

Training and/or utilization of a neural decompiler that can be used to generate, from a lower-level compiled representation, a target source code snippet in a target programming language. In some implementations, the lower-level compiled representation is generated by compiling a base source code snippet that is in a base programming language, thereby enabling translation of the base programming language (e.g., C++) to a target programming language (e.g., Python). In some of those implementations, output(s) from the neural decompiler indicate canonical representation(s) of variables. Technique(s) can be used to match those canonical representation(s) to variable(s) of the base source code snippet. In some implementations, multiple candidate target source code snippets are generated using the neural decompiler, and a subset (e.g., one) is selected based on evaluation(s).

Training and/or utilization of a neural decompiler that can be used to generate, from a lower-level compiled representation, a target source code snippet in a target programming language. In some implementations, the lower-level compiled representation is generated by compiling a base source code snippet that is in a base programming language, thereby enabling translation of the base programming language (e.g., C++) to a target programming language (e.g., Python). In some of those implementations, output(s) from the neural decompiler indicate canonical representation(s) of variables. Technique(s) can be used to match those canonical representation(s) to variable(s) of the base source code snippet. In some implementations, multiple candidate target source code snippets are generated using the neural decompiler, and a subset (e.g., one) is selected based on evaluation(s).