Package revng's python code in two wheels: `revng` and `revng_internal`.
The revng wheel contains the
`revng.{pipeline_description,model,tupletree}` modules, while the
`revng_internal` one everything under `revng.internal`.
Add the PipelineDescription object that describes the structure of a
pipeline. This will be used by all the downstream users instead of
pipeline accessor methods via PipelineC.
Add support for tracing onto the PipelineC. This is done by:
1. Creating wrapper functions for each PipelineC function with the
script in `scripts/PipelineC_add_tracing.py`. These will call a
special function called `wrap` which will ultimately call a method
with a `_` prepended to the name
2. Conversion of all PipelineC methods in `PipelineC.cpp` to `static`
and their rename with a `_` in front, in order for them to work with
the wrapper function in (1)
3. Generation of 2 additional include files, one for types and one for
functions, to be used by users of tracing files in order to have
introspection.
These steps allow the creation of a trace file with the use of the
`REVNG_C_API_TRACE_PATH` environment variable. The traces can then be
used in conjunction with the `revng trace run` and `revng trace
inspect` commands.
This new command will, given an executable, try to produce all
artifacts. By default it will run `revng daemon`, however an external
address can be provided to test remote daemons, e.g. running inside a
container.
This Oython tool will be used to easily work with PTML.
It currently features two subcommands:
* `cat` prints a PTML to the terminal, with color formatting if
possible;
* `strip` will strip a file of the PTML markup;
Add the possibility of passing a list of FIFOs to `revng daemon`. These
can be used to notify an external program when a non-reproducible change
(binary, context) has occurred.
Splits the module in 2:
* static_handlers: as the name suggests this is where handlers for
static endpoints (e.g. not dependent on the pipeline) are implemented
* schema_generator: this is where the schema is generated from a
manager's pipeline definition, together with the handler for the
autogenerated endpoint
`revng.api` now autodetects files (libraries, pipeline yamls) for
initialization via the same mechanism used by `revng.cli`. This removes
the need to pass them via environment variables from `revng daemon`
and allows easy interaction with python's C API.
This commit adds the newly implemented functionality in PipelineC both
in revng.api and the graphql api, allowing:
* retrieval of global variable names
* unwrapping of a single target
* execution of analyses
In the future we will need to use GraphQL subscriptions. This is done
via websockets and is supported in Ariadne. However this support is
limited to ASGI frameworks, which Flask isn't a part of.
Startlette is a direct depencency of Ariadne, and all of Ariadne's
features are fully integrated with Starlette, so the switch allows to
drop some Flask integration cruft and streamline the revng.daemon
package.
Starlette does not have a built-in development server, instead
Hypercorn, which is an ASGI-compliant HTTP server, is used in place of
Flask's Werkzeug/Gunicorn for both the development and production
server roles.
This commit introduces the `revng.daemon` Python module, a Flask-powered
web application that exposes the functionality from `revng.api` across a
GraphQL API
We used to collect all binaries into the `bin/` directory. However this
led to confusions since certain commands where available both as
`revng-command` and `revng command`.
This commit moves all the executables except `revng` into
`libexec/revng`, which, according to FHS, is dedicated to "internal
binaries that are not intended to be executed directly by users or shell
scripts".
Alessandro Di Federico