Overview of Testing in Tezos

The goal of this document is to give an overview on how testing is done in Tezos, and to help Tezos contributors use the test suite and write tests by pointing them towards the most appropriate testing framework for their use case. Finally, this guide explains how tests can be run automatically in the Tezos CI and how to measure test coverage.

The frameworks used in Tezos can be categorized along two axes: the type of component they test, and the type of testing they perform. We distinguish the following components:

  • Node

    • Protocol

      • Michelson interpreter

      • Stitching

  • Networked nodes

  • Client

  • Ledger application

  • Endorser

  • Baker

Secondly, these components can be tested at different levels of granularity. Additionally, tests can verify functionality, but also non-functional properties such as performance (execution time, memory and disk usage). We distinguish:

Unit testing

Unit testing tests software units, typically functions, in isolation.

Integration testing

Integration testing tests compositions of smaller units.

System testing

System testing tests the final binaries directly.

Regression testing

In general, regression testing aims to detect the re-introduction of previously identified bugs. It can also refer to a coarse-grained type of testing where the output of a test execution is compared to a pre-recorded log of expected output. We here use “regression testing” to refer to the second meaning.

Property testing / Fuzzing

Both property testing and fuzzing test code with automatically generated inputs. Property testing is typically used to ensure functional correctness, and gives the user more control over generated input and the expected output. Fuzzing is typically used to search for security weaknesses and often guides input generation with the goal of increasing test coverage.

Performance testing

Testing of non-functional aspects such as run-time, memory and disk usage.

Acceptance testing

Testing of the software in real conditions. It is usually slower, more costly and less amenable to automation than integration or system testing. It is often the final step in the testing process and is performed before a release. In Tezos, acceptance testing is done by running a test net.

By combining the two axes, we obtain the following matrix. Each cell contains the frameworks appropriate for the corresponding component and testing type. The frameworks are linked to a sub-section of this page where the framework is presented in more detail.

Testing frameworks and their applications in Tezos. PT: Python testing and execution framework, AT: Alcotest, PBT: QCheck, FT: Flextesa, TZ: Tezt












– Protocol



– – Michelson interpreter







Networked nodes






Testing frameworks


Alcotest is a library for unit and integration testing in OCaml. Alcotest is the primary tool in Tezos for unit and integration testing of OCaml code.

Typical use cases:
  • Verifying simple input-output specifications for functions with a hard-coded set of input-output pairs.

  • OCaml integration tests.

Example tests:
  • Unit tests for src/lib_requester, in src/lib_requester/test/test_requester.ml. To execute them locally, run dune build @src/lib_requester/runtest in the Tezos root. To execute them on your own machine using the GitLab CI system, run gitlab-runner exec docker unit:requester.

  • Integration tests for the P2P layer in the shell. For instance src/lib_p2p/test/test_p2p_pool.ml. This test forks a set of processes that exercise large parts of the P2P layer. To execute it locally, run dune build @runtest_p2p_pool in the Tezos root. To execute the P2P tests on your own machine using the GitLab CI system, run gitlab-runner exec docker unit:p2p. The job-name unit:p2p is ill-chosen, since the test is in fact an integration test.



QCheck is a library for property-based testing in OCaml.

Typical use cases:
  • Verifying input-output invariants for functions with randomized inputs.

Example test:
  • QCheck is used in src/lib_base/test/test_time.ml to test the Tezos_base.Time module. For instance, subtracting and then adding a random amount of seconds to a random time should give back the original time: this tests that add and diff are consistent (and the inverse of each other). To run this test, you need to run dune exec src/lib_base/test/test_time.exe.


Python testing and execution framework

The Tezos project uses pytest, a Python testing framework, combined with tezos-launchers, a Python wrapper tezos-node and tezos-client, to perform integration testing of the node, the client, networks of nodes and daemons such as the baker and endorser.

We also use pytest-regtest, a pytest plugin that enables regression testing.

Typical use cases:
  • Testing the commands of tezos-client. This allows to test the full chain: from client, to node RPC to the implementation of the economic protocol.

  • Test networks of nodes, with daemons.

  • Detecting unintended changes in the output of a component, using pytest-regtest.

Example tests:
  • Detecting unintended changes in the behavior of the node’s Michelson interpreter (in tests_python/tests_alpha/test_contract_opcodes.py). To execute it locally, run cd tests_python && poetry run pytest tests/test_contract_opcodes.py in the Tezos root. To execute them on your own machine using the GitLab CI system, run gitlab-runner exec docker integration:contract_opcodes.

  • Setting up networks of nodes and ensuring their connection (in tests_python/tests_alpha/test_p2p.py). To execute it locally, run cd tests_python && poetry run pytest tests/test_p2p.py in the Tezos root. To execute them on your own machine using the GitLab CI system, run gitlab-runner exec docker integration:p2p.



Flextesa (Flexible Test Sandboxes) is an OCaml library for setting up configurable and scriptable sandboxes to meet specific testing needs. Flextesa can also be used for interactive tests. This is used, for instance, in some tests that require the user to interact with the Ledger application.

Typical use cases:
Example test:


Tezt is a system testing framework for Tezos. It is intended as a replacement to Flextesa and as an OCaml-based alternative to Python testing and execution framework. Like the latter, Tezt is also capable of regression testing. Tezt focuses on tests that run in the CI, although it is also used for some manual tests (see the tezt/manual_tests folder). Its main strengths are summarized in its section in the Tezos Developer Documentation. Conceptually Tezt consists of a generic framework for writing tests interacting with external processes, and a set of Tezos-specific modules for interacting with the Tezos binaries: the client, baker, etc.

Typical use cases:
Example tests:
  • Testing baking (in tezt/tests/basic.ml)

  • Testing double baking and double endorsement scenarios (in tezt/tests/double_bake.ml). This test is a rewrite of the Flextesa double baking scenario mentioned above, that demonstrates the difference between the two frameworks.

  • Testing absence of regressions in encodings (in tezt/tests/encoding.ml)


Long Tests

Tezt is also used for tests that are too long to run in the CI. Those tests are run on dedicated machines and can send data points to an InfluxDB instance to produce graphs using Grafana and/or detect performance regressions. See Tezt: Long Tests.

Executing tests locally

Whereas executing the tests through the CI, as described below, is the standard and most convenient way of running the full test suite, they can also be executed locally.

All tests can be run with make test in the project root. However, this can take some time, and some tests are resource-intensive or require additional configuration. Alternatively, one can run subsets of tests identified by a specialized target test-*. For instance, make test-unit runs the alcotest tests and should be quite fast. See the project Makefile for the full list of testing targets.

Measuring test coverage

We measure test coverage with bisect_ppx. This tool is used to see which lines in the code source are actually executed when running one or several tests. Importantly, it tells us which parts of the code aren’t tested.

We describe here how bisect_ppx can be used locally. See below for usage with CI.

To install bisect_ppx, run the following command from the root of the project directory:

make build-dev-deps

The OCaml code should be instrumented in order to generate coverage data. This is done by prepending

COVERAGE_OPTIONS="--instrument-with bisect_ppx" BISECT_FILE=$(pwd)/_coverage_output/

to build and test commands run from the root of the project directory. For example,

COVERAGE_OPTIONS="--instrument-with bisect_ppx" BISECT_FILE=$(pwd)/_coverage_output/ make
COVERAGE_OPTIONS="--instrument-with bisect_ppx" BISECT_FILE=$(pwd)/_coverage_output/ make test-coverage

Generate the HTML report from the coverage files using

make coverage-report

The generated report is available in _coverage_report/index.html. It shows for each file, which lines have been executed at least once, by at least one of the tests.

Clean up coverage data (output and report) with:

make coverage-clean

If calling dune directly, instrumentation is achieved by setting BISECT_FILE environment variable to an existing directory and appending the flag --instrument-with. Let’s consider lib_mockup as an example:

cd src/lib_mockup
mkdir -p _coverage_output/
BISECT_FILE=$(pwd)/_coverage_output/ dune build --instrument-with bisect_ppx

Now, still in the src/lib_mockup directory, run test commands:

BISECT_FILE=$(pwd)/_coverage_output/ dune test -f --instrument-with bisect_ppx

In this folder, we do not have the make coverage-report. However, this target is simply a shortcut to the bisect-ppx-report binary. This command must be run from the root of the project:

cd ../../
bisect-ppx-report html -o _coverage_report_mockup --coverage-path src/lib_mockup/_coverage_output/

The report will now be found in _coverage_report_mockup.

Enabling instrumentation for new libraries and executables

To ensure that all libraries and executables are included in the coverage report, the following field should be added to all library and executable(s) stanzas in all dune files, e.g.:

  (name ...)
    (backend bisect_ppx)))

This enables the conditional instrumentation of the compilation unit through the --instrument-with bisect_ppx flag as described above.

Exempted from this rule are the dune files that belong to tests, developer utilities and old protocols. In particular:

  • benchmarks, e.g. src/lib_shell/bench/dune

  • bindings, e.g. src/lib_sapling/bindings/dune

  • test frameworks, e.g. src/bin_sandbox/dune

  • test packages, e.g. src/*/test/dune

  • old protocols, e.g. src/proto_00*/*/*dune

  • helper utilities, e.g.:

    • src/openapi/dune, (executable name openapi)

    • src/lib_client_base/gen/dune (executable name bip39_generator)

    • src/lib_protocol_compiler/dune (executable name replace)

    • src/proto_alpha/lib_parameters/dune (executable name gen)

    • src/proto_010_PtGRANAD/lib_parameters/dune (executable name gen)

    • src/lib_protocol_environment/s_packer/dune (executable name s_packer)

    • src/lib_store/legacy_store/dune (executable name legacy_store_builder)

Known issues

  1. Report generation may fail spuriously.

    $ make coverage-report
    4409 Info: found coverage files in '_coverage_output/'
    4410  *** invalid file: '_coverage_output/819770417.coverage' error: "unexpected end of file while reading magic number"

    In that case, either delete the problematic files or re-launch the tests and re-generate the report.

Executing tests through the GitLab CI

All tests are executed on all branches for each commit. For instances, to see the latest runs of the CI on the master branch, visit this page. Each commit is annotated with a green checkmark icon if the CI passed, and a red cross icon if not. You can click the icon for more details.

The results of the test suite on terminated pipelines is presented on the details of the merge request page corresponding to the pipeline’s branch (if any). For more information, see the GitLab documentation on Unit test reports.

By default, the test of the CI runs the tests as a set of independent jobs that cluster the tests with a varying grain. This strikes a balance between exploiting GitLab runner parallelism while limiting the number of jobs per pipeline. The grain used varies slightly for different types of tests:

Python integration and regression tests

We run one job per pytest test file whose tests are marked slow. We run one job regrouping the set of pytests per protocol that are not marked slow.

Tezt integration and regression tests

Tezt tests are grouped in 3 batch jobs. New tests increases the size of the last batch.

The OCaml package tests (Alcotest & QCheck)

The OCaml package tests are regrouped analogously to the pytests: one job per protocol package, in addition to one job regrouping tests for remaining packages.


Flextesa tests run in one job per test.

Adding tests to the CI

When adding a new test that should be run in the CI (which should be the case for most automatic tests), you need to make sure that it is properly specified in the .gitlab-ci.yml file. The procedure for doing this depends on the type of test you’ve added:

Python integration and regression tests

Run ./scripts/update_integration_test.sh in Tezos home. This will include your new test in .gitlab-ci.yml.

Tezt integration and regression tests

New Tezt tests will be included automatically in the CI. To rebalance the Tezt batches, run (from the root of the Tezos repository): make && dune exec tezt/tests/main.exe -- --record tezt/test-results.json

The OCaml package tests (Alcotest & QCheck)

Any non-protocol tests located in a folder named src/**/test/ will be picked up automatically by the CI. No intervention is necessary.

Protocol tests must be added to .gitlab/ci/unittest.yml under the protocol that they are testing. For example, to run a new protocol test for proto_XXX_YYYYYYYY, add the corresponding src/proto_XXX_YYYYYYYY/lib_\*.test_proto to the unit:XXX_YYYYYYYY make invocation.

Other (including Flextesa)

For other types of tests, you need to manually modify the .gitlab-ci.yml. Please refer to the GitLab CI Pipeline Reference. A helpful tool for this task is the CI Lint tool, and gitlab-runner, introduced in the next section.

Measuring test coverage in the CI

To measure test coverage in the CI, trigger the jobs test_coverage[...] in stage test_coverage These jobs can either be triggered manually from the Gitlab CI web interface, or by pushing a branch whose name contains coverage. These jobs build Octez with coverage instrumentation, and then run respectively:

  • make test-unit, executing all unit, integration and property-based tests

  • make test-python-alpha, executing the Python system tests for protocol Alpha. We restrict to protocol Alpha to avoid CI timeouts.

  • make test-tezt-timeout, executing the full set of Tezt tests with a timeout of 30 minutes per test case.

Finally the coverage reports are generated: one for unit tests, one for the Python system tests and one for the Tezt suite.

The resulting coverage reports are stored as artifacts that can be downloaded or browsed from the CI page upon completion of the job.

Executing the GitLab CI locally

GitLab offers the ability to run jobs defined in the .gitlab-ci.yml file on your own machine. This is helpful to debug the CI pipeline. For this, you need to setup gitlab-runner on your machine. To avoid using outdated versions of the binary, it is recommended to install a release from the development repository.

gitlab-runner works with the concept of executor. We recommend to use the docker executor to sandbox the environment the job will be executed in. This supposes that you have docker installed on your machine.

For example, if you want to run the job check_python_linting which checks the Python syntax, you can use:

gitlab-runner exec docker check_python_linting

Note that the first time you execute a job, it may take a long time because it requires downloading the docker image, and gitlab-runner is not verbose on this subject. For instance, if Tezos’ opam repository has changed, requiring a refresh of the locally cached docker image.

Local changes must be committed (but not necessarily pushed remotely) before executing the job locally. Indeed, gitlab-runner will clone the head of the current local branch to execute the job.

Another limitation is that only single jobs can be executed using gitlab-runner. For instance, there is no direct way of executing all jobs in the stage test. However, you can run the test_coverage job which runs most tests (alcotest and python tests) in a single job.

gitlab-runner exec docker test_coverage


Besides implementing tests, it is necessary to comment test files as much as possible to keep a maintainable project for future contributors. As part of this effort, we require that contributors follow this convention:

  1. For each unit test module, add a header that explains the overall goal of the tests in the file (i.e., tested component and nature of the tests). Such header must follow this template, and be added after license:

(** Testing
    Component:    (component to test, e.g. Shell, Micheline)
    Invocation:   (command to invoke tests)
    Dependencies: (e.g., helper files, optional so this line can be removed)
    Subject:      (brief description of the test goals)
  1. For each test in the unit test module, the function name shall start with test_ and one must add a small doc comment that explains what the test actually asserts (2-4 lines are enough). These lines should appear at the beginning of each test unit function that is called by e.g. Alcotest_lwt.test_case. For instance,

(** Transfer to an unactivated account and then activate it. *)
let test_transfer_to_unactivated_then_activate () =
  1. Each file name must be prefixed by test_ to preserve a uniform directory structure.

  2. OCaml comments must be valid ocamldoc special comments.