A decompilation apparatus includes a matcher that divides captured first program code for a processor of a first processor architecture into a sequence of code sections based on a predetermined set of at least partially parameterized code patterns, wherein the first program code implements a program logic and has been generated by compiling a computer program defined in a high-level language by a compiler, and the matcher captures for each code section specific parameter values for each parameter of a corresponding parameterized code pattern and assigns to each code section a terminal symbol of an intermediate language; and a parser that reduces a sequence of terminal symbols assigned to the sequence of code sections to non-terminal symbols of the intermediate language, wherein a totality of the non-terminal symbols generated by the parser by reduction describes the program logic of the computer program in the intermediate language.

An electronic device is provided. The electronic device includes a memory configured to store at least one application, a database storing determination criteria information to determine an abnormal operation of the at least one application, and an agent configured to control a process of the at least application, and a processor, coupled to the memory, configured to execute the agent, in which, when executing the agent, the processor is configured to receive state information related to the abnormal operation of the at least one application from an operating system (OS), determine a target application from the at least one application in which an abnormal operation is expected based on the determination criteria information, and preload the target application in a background.

A user device includes a memory and one or more processors. The memory is configured to store one or more user applications installed in the user device. The one or more processors are configured to run an Operating System (OS) of the user device, including maintaining a data structure that tracks activity of the user applications, and to pre-load a user application before the user application is accessed by a user, including preventing the data structure from tracking the pre-loaded user application.

A method for establishing an application prediction model, a storage medium, and a terminal are provided. The method includes the following. In response to an operation switching an application previously running in the foreground to a first application currently running, a first preorder usage sequence of the first application currently running is collected as sample usage information. Applications launched within a time-window are monitored, and a preset number of applications launched first in the applications launched within the time-window are set as sample labels for the sample usage information. A predetermined machine learning model is trained based on the sample usage information and the sample labels for the sample usage information, to obtain the application prediction model.

An application optimization method, a storage medium, and an electronic equipment are provided, wherein the method is applied to the electronic equipment executing following steps: determining an application to be optimized in the electronic equipment through a system process; obtaining a count of interface switches of the application to be optimized; when the count of interface switches of the application to be optimized is greater than a predetermined count, obtaining a data volume of a file of the application to be optimized; and when the data volume is greater than a preset threshold, optimizing the file of the application to be optimized. The application is not required to wait for 24 hours before optimization, and can be optimized promptly according to the condition of the application, so the optimization efficiency of the application is improved, and the performance of the application is also improved.

The disclosed computer-implemented method may include predicting, by a machine learning model, a user action and a timing of the user action for an application on a computing device. The method may also include determining that an expected lag in executing the user action is greater than a predetermined threshold based on one or more resource constraints of the computing device and initializing a setup of the application to reduce the expected lag in executing the user action based on the predicted timing. Furthermore, the method may include prefetching one or more application components in response to initializing the setup of the application and preprocessing at least a portion of a function of the application used to execute the user action. Finally, the method may include executing the user action for the application in response to a user request. Various other methods, systems, and computer-readable media are also disclosed.

A method includes, in a processor of a user device, deciding to preload a user application, which has one or more User Interface (UI) displays whose state is retained by the processor in a memory of the user device. At least part of the user application is preloaded, and a state of the preloaded user application is restored, in a background mode, to match the retained state of the one or more UI displays.

Based on power on of an electronic device, a location of first data in a NAND flash memory of an electronic device is determined. The first data is transmitted to a shadow RAM of the electronic device, outputting the first data is output from the shadow RAM to a host device of the electronic device through a serial peripheral interface (SPI) when accessing the location of the first data in the NAND Flash memory.

One embodiment provides a computer implemented computer implemented method, including: receiving information describing an application to be split into a plurality of modules to be hosted on a remote services environment; identifying a plurality of possible sets of modules for splitting the application; providing a visualization of the application split into modules, wherein the visualization displays different possible sets of modules as different layers within the visualization; and splitting, responsive to receiving user input within the visualization selecting one of the different layers, the application into one of the different possible sets of modules based upon the one of the different layers.

A user device includes a memory and one or more processors. The memory is configured to store one or more user applications installed in the user device. The one or more processors are configured to select one or more operations, which are to be performed in normal launching of a user application but not in background pre-loading of the user application, to pre-load the user application before the user application is accessed by a user, including skipping the one or more selected operations, and to complete the one or more skipped operations in response to the user accessing the user application.