Where we are, where want to go

Building a decompiler is a big endeavour. Follow our latest advancements and the plan for the months to come towards the 1.0 release.

Tier 2: Closed beta (part 1)

Adopt new Hub frontend

Dismiss the old Web 1.0 Hub frontend in favor of the new Next.js-based Hub frontend.

Tier 2 Public Relations

Publish the new website, release a blog post, send newsletter, write tutorial for Tier 2 participants.

No timeouts/crashes on selected binaries

Make sure we can run the whole pipeline on a predetermined set reasonably-sized x86-64 Linux binaries without crashes and within a reasonable time frame.

Perform in-depth QA on `hostname`

Make sure the hostname binary is decompiled in a sensible way.

Tier 2.5: Closed beta (part 2)

Mass testing

Test on a massive amount of binaries and promote binaries that are decompiled without crashes and withing a reasonable time frame to the regression suite.

Implicit conversions

Detect and remove casts that in C would be implicit. This will significantly reduce the number of casts the user sees in the decompiled code.


Perform Quality Assurance on EFA results on a vast number of functions with a diverse set of arguments and return values.

Push variable declarations ALAP

In decompiled C code, make sure we declare local variables as late as possible.

Implement verification passes

Implement passes whose role is to assert certain invariants are preserved in each part of the pipeline.

Primitives inlining

Change the model layout so that primitive types (e.g., uint32_t) are defined inline, instead of having an entry in model::Binary::Types.


Implement the 4th version of Early Function Analysis, which will significantly improve detection of register-based arguments and return values.

Invalidation logic

Implement the logic to detect what artifacts needs to be recomputed, instead of recomputing everything at every change.


Provide public documentation of the model, the CLI and our Python/TypeScript wrappers.

Tier 3: Open beta (part 1)

CRUD all model parts in UI

In the UI we need to provide a way to create, edit and remove types, functions, segments and so on.

Initial auto-analyses twice

We need to be able to run the analysis pipeline twice without crashing.

Rebase QEMU

Rebase QEMU to the latest version. This will enable us to support additional architectures and start working on for proper floating point support.

Preserve debug info

Review the decompilation pipeline to ensure that debug information, which we use to trace decompiled code back to assembly instructions, are preserved as much as possible. This ensure we don't lose the link between decompiled code and assembly in most situations.

Model verify on the client

Enable the VSCode client to verify the model without making a remote request. This ensures that the user can make interactive changes and immediately have a feedback if the changes are valid or not.

Drop kinds

Get rid of kinds from revng-pipeline.

Improve C state variables

Rework how we name artificial state variables introduced, e.g., to exit from a loop.

Full backward navigation

Make sure the UI can perform backward navigation even between references that are not available in the call graph. This might require to materialize all artifacts in background.

Model upgrade

Implement infrastructure to automatically upgrade among model versions.

Propagate types in IR before DLA

Perform type propagation through the IR before DLA is run. This ensures DLA can reconstruct richer types, such as signed and unsigned integers.

Collaboration QA

Ensure collaboration works smoothly.


Implement a basic hexadecimal view.

Support multiple binaries

Make sure a single project can handle multiple projects. Also, switch to record hashes of binaries in the model, instead of asking the user to provide it.

Improve inlining

The Inline attribute of model::Function has known limitations. Make sure we can inline any function.

Hub: expose snippets

Implement in Hub a feature to embed decompiled code snippets.

Implement undo/redo

Implement the undo/redo feature.

Reorganize repositories

Merge the revng and revng-c repositories.


Implement a more git-like CLI for revng and move most of the revng-pipeline logic to Python.

Python client

Implement a dev-friendly Python library to interact with revng-daemon's GraphQL API.

Declutter the UI

Make the necessary changes to VSCode to remove everything that's not strictly necessary for our use case.

Tier 3.5: Open beta (part 2)


Implement support for in dogbolt.

Perform QA on various architectures

We need to fix platform-specific issues, bug and limitations that pop up on architectures that have not gone through QA yet.

Support variadic arguments

Implement support for variadic arguments for the various ABIs we support.

Floating point support

Improve support for floating point instruction and data types.

Segment with designated initializers

In the C view, show segments as global variables using C's designed initializers.

Tidy model.h

Cleanup the C headers we emit.

Import stack frames from DWARF

Exploit information in DWARF debug info to automatically populate the stack frame of functions.

Strings view

Implement a simple view to show all the strings we detected in the binary.

DLA: import model + subgraph

Ensure DLA can import existing model information and can correctly run on a portion of the call graph. This will enable us to re-run DLA after the initial analysis.

Detect Strings

Add analysis to detect string literals in segments.

Tier 4: 1.0 release

ConvertToCABI QA

Perform Quality Assurance on the result of converting RawFunctionTypes to CABIFunctionTypes.

Background artifacts production

Implement logic to asynchronously produce artifacts in background.

Patches raw bytes in model

Add support for patching the binary from the model.


Implement a view that shows, as a single document, the whole binary.


Implement an online shop to buy subscriptions/licenses.

Register to the UI closed beta!

Want to try the UI? We're now inviting people on a FIFO basis.