Systems and methods are provided for implementing a machine learning (ML) model based compiler, language translator, and/or a decompiler. For example, the system may receive a first source code in a first programming language, tokenize the first source code file forming tokenized code, generate a sequence vector of tokenized code, and provide the sequence vector of tokenized code as input to a trained ML model compiler. The output of the trained ML model compiler may create a second executable file or the source code in a second programming language.

Various computing technologies for various reverse engineering platforms capable of outputting, including creating or generating, a human readable and high level source code, such as C, Fortran, LISP, or BASIC, from various binary files, such as application binaries, executable binaries, or data binaries, in an original language as developed pre-compilation. For example, some of such reverse engineering platforms can be programmed to disassemble binary files from different process architectures, identify various code optimizations as compiler introduced, reverse or unwind various compiler optimizations (de-optimize), and generate a human readable and high-level source code from de-optimized data.

A system is disclosed for automatic retrieval and analysis of PLC code including at least one code disassembler configured to receive, via one or more passive or active scanners connected to an automation network, compiled code configured for execution on one or more devices of the automation network, the at least one code disassembler automatically disassembling the compiled code into program code. The system including an analyzing component configured to receive the program code from the code disassembler and to automatically analyze the program code for errors. The system including an alerting component configured to receive one or more detected errors from the analyzing component and to communicate or store the one or more detected errors.

A system is disclosed for automatic retrieval and analysis of PLC code including at least one code disassembler configured to receive, via one or more passive or active scanners connected to an automation network, compiled code configured for execution on one or more devices of the automation network, the at least one code disassembler automatically disassembling the compiled code into program code. The system including an analyzing component configured to receive the program code from the code disassembler and to automatically analyze the program code for errors. The system including an alerting component configured to receive one or more detected errors from the analyzing component and to communicate or store the one or more detected errors.

This application discloses code processing methods, apparatuses, and storage media. An example method includes: obtaining a first code in low-level language and applicable to a source platform; decompiling the obtained first code to obtain an intermediate representation (IR); and then compiling the IR into a second code in low-level language and applicable to a first target platform, where the source platform and the target platform have different instruction sets.

Tools and techniques are described to automate line testing when wiring devices (such as equipment and sensors) to controllers. Controllers have access to databases of the devices that are controlled by them, including wiring diagrams and protocols, such that the controller can automatically check that each wire responds correctly to stimulus from the controller. After testing, a reporting device rapidly shows the results of the line testing.

Tools and techniques are described to automate line testing when wiring devices (such as equipment and sensors) to controllers. Controllers have access to databases of the devices that are controlled by them, including wiring diagrams and protocols, such that the controller can automatically check that each wire responds correctly to stimulus from the controller. After testing, a reporting device rapidly shows the results of the line testing.

The present disclosure relates to a system, method, and computer program for testing the accuracy of software that identifies user interface (UI) elements on a webpage (“the auto-identifier software”). The system enables a user to tag UI elements on a webpage with labels. The system creates a normalized specification for the webpage, where the specification includes a mapping of UI elements to normalized labels. The system uses the auto-identifier software to identify UI elements on the webpage. The system evaluates the performance of the auto-identifier software with respect to the webpage using the specification. The system displays diagnostics related to the performance of the auto-identifier software. In certain embodiments, the method is used for testing the accuracy of autofill software on a webform. In certain embodiments, the method is used for testing the accuracy of cart-scraper software on a checkout page.

A method includes obtaining a binary code of a controller. The method also includes decompiling the binary code of the controller to generate a source code. The method further includes generating one or more abstract syntax trees based on the source code. The method further includes generating an interpretable model based on the one or more abstract syntax trees. The interpretable model is interpretable by subject matter experts.

A method includes obtaining a binary code of a controller. The method also includes decompiling the binary code of the controller to generate a source code. The method further includes generating one or more abstract syntax trees based on the source code. The method further includes generating an interpretable model based on the one or more abstract syntax trees. The interpretable model is interpretable by subject matter experts.