PROGRAMMABLE ELECTRONIC TEST EQUIPMENT AND METHOD FOR PROGRAMMING ELECTRONIC TEST EQUIPMENT

    公开(公告)号:US20240192933A1

    公开(公告)日:2024-06-13

    申请号:US18077510

    申请日:2022-12-08

    摘要: Flexibly programmable electronic test equipment and methods for programming same are disclosed. A piece of electronic test equipment includes signal processing hardware, a signal processor coupled to the signal processing hardware to control operation of the signal processing hardware, a central processor coupled to the signal processor, a graphical user interface, a graphics processor configured to control operations of the graphical user interface, a script language interpreter including a compiler and a virtual machine, the compiler converts script source code to byte code fed to the virtual machine interprets the byte code into machine code at run-time for controlling at least one of the signal processor and the graphics processor, the graphical user interface receives editable input parameters from a user to the script source code being converted by the compiler of the script language interpreter.

    SYSTEM AND METHOD THAT ASSISTS WITH IDENTIFYING UNPREDICTED PORTIONS OF SOURCE CODE FILES FOR SOFTWARE ENGINEERING TASKS

    公开(公告)号:US20240184536A1

    公开(公告)日:2024-06-06

    申请号:US18524447

    申请日:2023-11-30

    申请人: Laredo Labs, Inc.

    IPC分类号: G06F8/30

    CPC分类号: G06F8/30

    摘要: A system stores a source code file's changes from a software developer's code editor, for a software engineering task. Upon receiving the code editor's request to predict source code for the source code file, the system retrieves the software engineering task's context data, and transforms the context data to be compatible with the data format used to train a machine-learning model to assist with performing software engineering tasks. The machine-learning model uses the transformed context data to predict the source code for the source code file, with source code file portions corresponding to predicted source code portions. The system identifies each portion of the source code file which is differing from a corresponding portion of the predicted source code, via the code editor. The system commits any differing portions of the predicted source code, which are requested and accepted by the code editor, to the source code file.