Overview of Testing in Tezos¶

Testing is important to ensure the quality of the Tezos codebase by detecting bugs and avoiding regressions. Tezos and its components use a variety of tools and frameworks for testing. 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¶
Component	Unit	Property	Integration	System	Regression
Node	AT	PBT	AT	PT, FT, TZ
– Protocol	AT	PBT
– – Michelson interpreter	AT			PT	PT
Client		PBT		PT, FT, TZ
Networked nodes	–		PT, FT
Endorser				FT
Baker				FT

Testing frameworks¶

Alcotest¶

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.

References:

Alcotest README.

QCheck¶

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.

References:

Crowbar¶

Crowbar is a library for property-based testing and fuzzing in OCaml. It also interfaces with afl to enable fuzzing.

Typical use cases:

Verifying input-output invariants for functions with randomized inputs.

Example test:

Crowbar is used in data-encoding, a Tezos component that has been spun off into its own opam package. For instance, data-encoding uses Crowbar to verify that serializing and deserializing a value results in the initial value. To run this test, you need to checkout and build data-encoding. Then, run dune @runtest_test_generated.

References:

Crowbar README

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.

References:

Flextesa¶

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:

In terms of use cases, Flextesa is similar to the Python testing and execution framework.

Example test:

Testing double baking, accusations and double-baking accusation scenarios (in src/bin_sandbox/command_accusations.ml)

References:

Tezt¶

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:

In terms of use cases, Tezt is similar to the Python testing and execution framework and Flextesa. It can be used by authors that prefer OCaml for writing system tests.

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)

References:

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 has to be specified in dune files (or dune.inc for protocols) on a per-package basis by adding the following line in the library or executable stanza.

(preprocess (pps bisect_ppx -- --bisect-file /path/to/tezos.git/_coverage_output))))

At the same time, it tells bisect_ppx to generate coverage data in the _coverage_output directory. The convenience script ./scripts/instrument_dune_bisect.sh does this automatically. For instance,

./scripts/instrument_dune_bisect.sh src/lib_p2p/dune src/proto_alpha/lib_protocol/dune.inc

enables code coverage analysis for lib_p2p and proto_alpha. To instrument all the code in src/, use:

./scripts/instrument_dune_bisect.sh src/

Then, compile the code using make, ignoring warnings such as .merlin generated is inaccurate. which are expected. Finally run any number of tests, and 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

To reset the updated dune files, you may either use git:

git checkout -- src/lib_p2p/dune src/proto_alpha/lib_protocol/dune.inc

or use the --remove option of the instrumentation script:

./scripts/instrument_dune_bisect.sh --remove src/

Known issues¶

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.

Instrumented code doesn’t compile on certain pattern-matching constructs with bisect_ppx <= 2.5.0

    File "src/proto_008_PtEdoTez/lib_protocol/script_ir_translator.ml", line 1074, characters 13-14:
    1074 |   | Lambda_t _

    Error: This pattern matches values of type $5 ty
        but a pattern was expected which matches values of type 'a ty
        The type constructor $5 would escape its scope

This issue is solved in the development version of ``bisect_ppx``. It can
be obtained with

::

    opam pin add bisect_ppx.2.5.0 --dev-repo --yes

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, Crowbar & 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: 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, Crowbar & QCheck): Run ./scripts/update_unit_test.sh in Tezos home. This will include your new test in .gitlab-ci.yml.
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.

Launching tests manually and measuring coverage in the CI¶

Another way to run the tests is to trigger manually the job test_coverage in stage test_coverage, from the Gitlab CI web interface. This job simply runs dune build @runtest in the project directory, followed by make all in the directory tests_python. This is slower than the previous method, and it is not run by default.

The role of having this extra testing stage is twofold.

It can be launched locally in a container environment (see next section),
it can be used to generate a code coverage report, from the CI.

The coverage report artefact can be downloaded or browsed from the CI page upon completion of test_coverage. It can also be published on a publicly available webpage linked to the gitlab repository. This is done by triggering manually the pages job in the publish_coverage stage, from the Gitlab CI web interface.

Up to a few minutes after the pages job is completed, the report is published at the URL indicated in the log of the pages job. The actual URL depends on the names of the GitLab account and project which triggered the pipeline, as well as on the pipeline number. Examples: https://nomadic-labs.gitlab.io/tezos/105822404/, https://tezos.gitlab.io/tezos/1234822404/.

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

Conventions¶

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:

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)
*)

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 () =
...

Each file name must be prefixed by test_ to preserve a uniform directory structure.
OCaml comments must be valid ocamldoc special comments.