Code profiling enables a user to know where in an application or function the execution time is spent. The Pharo ecosystem offers several code profilers. However, most of the publicly available profilers (MessageTally, Spy, GadgetPro-filer) largely ignore the activity carried out by the virtual machine, thus incurring inaccuracy in the gathered information and missing important information, such as the Just-in-time compiler activity. This paper describes the motivations and the latest improvements carried out in VMProfiler, a code execution profiler hooked into the virtual machine, that performs its analysis by monitoring the virtual machine execution. These improvements address some limitations related to assessing the activity of native functions (resulting from a Just-in-time compiler operation): as of now, VMProfiler provides more detailed profiling reports, showing for native code functions in which bytecode range the execution time is spent.