implementations -> Different implementations to be learned.
adapters -> Protocol adapters for alphabet translation.
learner -> Java based abstract learner.

Make sure you have Docker installed in your machine, and an internet connection to fetch/build the required Docker images. The source for all images is provided, however you may prefer using the pre-built Docker images for heavy implementations.

This tool has been tested with Docker 20.10.5. Apple Silicon / ARM support is provided on a best effort basis, if you'd like to run on ARM with emulation, be sure to add platform: linux/amd64 to every service in docker-compose.yaml.

Make sure all git modules are initiated:

git submodule init
git submodule update

If you would like to reproduce results from the paper, please download them from this link, as they are too big to be held by Git.

The various components work together through Docker Compose. By switching the image tag of the implementation container in docker-compose.yaml, we can work on different protocols and implementations. Available protocol adapters: quic, tcp. Available implementation tags: quiche, msquic, proxygen, googlequic, tcp.

After that we can fine tune the learning process further. The setting for which are grouped in the root config.yaml file. Its syntax is as follows:

learner:
    runsPerQuery: 3 # Minimum number of runs per query (non-determinism detection).
    confidence: 85 # Required confidence in query answer for it to be used.
    maxAttempts: 100 # Max attempts per query, after which to declare the system non-det. and terminate.
    sutPort: 3333 # Port used to communicate with the adapter.
    sutIP: adapter # Adapter IP/DNS name.
    inputAlphabet: # List of symbols to learn a model over.
        - SYN(?,?,0)
        - SYN+ACK(?,?,0)
        - ACK(?,?,0)
adapter:
    adapter:
    adapterAddress: 0.0.0.0:3333 # socket to listen on.
    sulAddress: implementation:4433 # Implementation address.
    sulName: quic.tiferrei.com # Implementation name (used by QUIC)
    http3: true # Use HTTP3 (instead of HTTP2).
    tracing: false # Use debug tracing in output.
    waitTime: 400ms # Period of time to wait for a response from SUL.
    httpPath: / # HTTP base request path to query.
synthesizer:
    op1: 0

You will find the appropriate config.yaml settings for each model learned in their respective folder in results.

We can now start the fully automated learning process with:

docker-compose up --remove-orphans learner

⚠️ WARNING: Synthesising rich models can be a memory intensive operations. Ensure Docker runs with at least 8GB of memory, 12GB recommended.

docker-compose up --remove-orphans synthesizer

Do note that the synthesis process purposefully does not terminate, and is in constant iteration. You will see the iterations starting to be written to output/synth, and the process can be stopped at any satisfactory time.

docker build -t analysis .
docker run analysis

Synthesis/det.py will display the number of traces required for some depth before and after learning.

This can happen for a variety of reasons. Some are:

This issue is caught by the error: SEVERE: Non-determinism found by probablistic oracle for input. This can be caused by several things. The most common is that the implementation being learned is not very stable, and after many queries will get into a non-deterministic state. If this is the case, simply restarting the process (with a fresh implementation container) will solve the issue. Previous queries are cached, so no progress is lost.

If this does not solve the issue then it might be that the waitTime needs to be increased to allow for packets arriving at inconsistent times.

If this still does not solve the issue, the query might indeed be non-deterministic, and thus cannot be learned by a deterministic learner.

This issue can happen when the answer to a new query is incompatible with the answer to a previous, shorter one. This is usually caused by the number of minQueries not being high enough, and thus not correctly capturing non-determinism. The safest option is to increase this parameter in the config.yaml and learn from scratch (by deleting the output folder and re-running).