Hotfix

Abstract: The embodiment of the invention provides a function call relation determining method, a hotfix updating method and a hotfix updating device. The function call relation determining method comprises steps that if the condition that a system calls a function is judged, and a stack frame start address of a parent function of the called function is not pressed into the stack, at least one of returning addresses in the stack which comprises the at least one returning address, is searched in sequence; when the at lest one returning address in the at least one of the returning addresses is searched, the function corresponding to the currently searched returning address is determined, and the recorded function call relation in the record list is amended according to the function corresponding to the returning address, so that the parent function of the called function in the amended function call relation is the function corresponding to the returning address. According to technical scheme provided by the embodiment of the invention, the function call relation can be more precisely determined according to the function name in the record list.

Abstract: The invention provides an Android application hotfix method and device. The method comprises the steps of connecting an Android application into a third-party software development kit; through comparing and matching, selecting a dynamic link library file corresponding to an optimal CPU (Central Processing Unit) framework in the third-party software development kit; and dynamically loading codes in a version directory and the dynamic link library file by utilizing a dexclassloader to finish the hotfix. According to the Android application hotfix method and device, a CPU framework in a host application operation environment can be automatically identified and adapted on the premise of a CPU framework in an unknown host application operation environment, and a hotfix function is finished; and meanwhile the version directory can be controlled and switched.

Abstract: Machine Learning is often associated with predictive analytics, for example with the prediction of buying and termination behavior, with maintenance times or the lifespan of parts, tools or products. However, Machine Learning can also serve other purposes such as identifying potential errors in a mission-critical large-scale IT process of the public sector. A delay of troubleshooting can be expensive depending on the error's severity- a hotfix may become essential. This paper examines an approach, which is particularly suitable for Static Code Analysis in such a critical environment. For this, we utilize a specially developed Machine Learning based approach including a prototype that finds hidden potential for failure that classical Static Code Analysis does not detect.

Abstract: The embodiment of the invention provides a patch generation method, a patching method and a patching device, and relates to the field of computers With adoption of the methods and the device, a hotfix can be patched conveniently on a function through inline optimization to provide guarantee for rapid repairing of an application program through an inline function The patching method comprises the following steps: performing function inline on the inline function and the inlined function to generate a third function; according to the third function, generating an equivalent code segment equivalent to the third function; obtaining an address for storing the inline function and the inlined function; obtaining a first repairing function for repairing the inlined function; according to the address, replacing the inlined function in the equivalent code segment by the first repairing function to generate a patch file; and outputting the address and the patch file to CPE (customer premise equipment) to enable the CPE to repair the inlined function according to the address and the patch file