This patch changes the way frames created from scripted affordances like
Scripted Threads are displayed. Currently, they're marked artificial
which is used usually for compiler generated frames.
This patch changes that behaviour by introducing a new synthetic
StackFrame kind and moves 'artificial' to be a distinct StackFrame
attribut.
On top of making these frames less confusing, this allows us to know
when a frame was created from a scripted affordance.
rdar://155949703
Signed-off-by: Med Ismail Bennani <ismail@bennani.ma>
This PR removes the `target-aarch64` requirement on the crashlog tests
to exercice them on Intel bots and make image loading single-threaded
temporarily while implementing a fix for a deadlock issue when loading
the images in parallel.
Signed-off-by: Med Ismail Bennani <ismail@bennani.ma>
Sometimes, crash reports come with inlined symbols. These provide the
exact stacktrace from the user binary.
However, when investigating a crash, it's very likely that the images related
to the crashed thread are not available on the debugging user system or
that the versions don't match. This causes interactive crashlog to show
a degraded backtrace in lldb.
This patch aims to address that issue, by parsing the inlined symbols
from the crash report and load them into lldb's target.
This patch is a follow-up to 27f27d1, focusing on inlined symbols
loading from legacy (non-json) crash reports.
To do so, it updates the stack frame regular expression to make the
capture groups more granular, to be able to extract the symbol name, the
offset and the source location if available, while making it more
maintainable.
So now, when parsing the crash report, we build a data structure
containing all the symbol information for each stackframe. Then, after
launching the scripted process for interactive mode, we write a JSON
symbol file for each module, only containing the symbols that it contains.
Finally, we load the json symbol file into lldb, before showing the user
the process status and backtrace.
rdar://97345586
Differential Revision: https://reviews.llvm.org/D146765
Signed-off-by: Med Ismail Bennani <ismail@bennani.ma>
