Requirements resolver


Willian Rampazzo <willianr@redhat.com>




Beraldo Leal, Cleber Rosa




Architecture Blueprint




The current management of test assets is handled manually by the test developer. It is usual to have a set of repetitive code blocks to define the name, location, and other attributes of an asset, download it or signal an error condition if a problem occurred and the download failed.

Based on use cases compiled from the discussion on qemu-devel mailing-list [1] and discussions during Avocado meetings, this blueprint describes the architecture of a requirements resolver aiming the extensibility and flexibility when handling different types of assets, like a file, a cloud image, a package, a Git repository, source codes or Operating System parameters.


Implementing a test that gathers its requirements while executing may lead to a wrong interpretation of the test results if a requirement is not satisfied. The failure of a test because of a missing requirement does not mean the test itself failed. During its execution, the test has never reached the core test code; still, it may be considered a failing test.

Fulfilling all the test requirements beforehand can be an efficient way to handle requirements problems and can improve the trustworthiness of the test result. It means that if a test ran and failed, the code responsible for the failure is related to the core test and not with one of its requirements.

Regardless of how the test defines a requirement, an architecture capable of identifying them is beneficial. Storing its references and delegating to the code responsible for handling each different type of requirement makes the overall architecture of Avocado and the requirement definition of a test more flexible.

A requirements resolver can bring the necessary flexibility to the Avocado architecture, as well as managing support for different types of requirements.

This blueprint discusses the architecture of a requirements resolver responsible for handling the different requirements types.



The strict meaning of a resolver is related to something responsible for creating resolutions from a given representation. When there is a well-defined way to declare something, a resolver can translate this representation to another well-defined representation. The classic example is a Domain Name Server (DNS), which resolves the hostname into an Internet Protocol (IP) address. The use of the word resolver in this text means a code responsible for gathering and fulfilling well-know representations with little or no transformation.

The definition of requirements resolver in this blueprint is a code responsible for gathering well-known formats of requirements, possibly from different sources, and centralizing in one place, or fulfilling them. The requirements fulfillment can take place starting from the centralized collection of requirements as input to one of several modules responsible for handling each specific type of requirement, like, for example, files, images, packages, git repositories, source code or operating system parameters.

The following diagram shows the underlying architecture of a requirements resolver proposed in this blueprint. The next sessions describes, in detail, each part of the resolver, its inputs, and outputs.

Requirements resolver diagram

Requirements representations

Define how to represent a requirement is the first step to define the architecture of a resolver. This blueprint defines the following ways to represent a requirement:

  1. JavaScript Object Notation (JSON) file;

  2. Python executable that produces a JSON file;

  3. Metadata included in the test docstring.

Requirements representation as JSON files

JSON is a lightweight data-interchange format [2] supported by the Python standard library. Using it to represent requirements is flexible and straightforward.

The standard proposed way to represent requirements with JSON is defining one requirement per entry. Each entry should start with the requirement type, followed by other keyword arguments related to that type. Example:

    {"type": "file", "uri": "https://cabort.com/cabort.c", "hash": "deadbeefdeadbeef"},
    {"type": "vmimage", "distro": "fedora", "version": 31, "arch": "x86_64"},
    {"type": "package", "package": "lvm"}

The requirement type should match the module responsible for that type of requirement.

Requirements representation as Python executable

Another way to create the requirements representation as JSON files is by writing a Python executable. This approach makes the requirements representation flexible, by allowing the use of Python variables and code that may change the parameters values for the requirements, depending on the environment the Python code runs.

The following example shows a requirement that depends on the architecture the test is running:


import os
import json

requirements = [
    {"type": "file", "uri": "https://cabort.com/cabort.c", "hash": "deadbeefdeadbeef"},
    {"type": "vmimage", "distro": "fedora", "version": 31, "arch": os.uname()[4]},
    {"type": "package", "package": "lvm"}


Requirements representation as Metadata on test docstring

Test writers may want to add the requirements of a test into de test code. The option proposed here allows the use of metadata on test docstrings to represent the requirements list.

Below is an example of how to define requirements as metadata on docstrings:

def test_something(self):
    :avocado: requirement={"type": "file", "uri": "https://cabort.com/cabort.c", "hash": "deadbeefdeadbeef"}
    :avocado: requirement={"type": "vmimage", "distro": "fedora", "version": 31, "arch": "x86_64"}
    :avocado requirement={"type": "package", "package": "lvm"}
   <test code>

Requirements files location

It may be useful for test writers to define a standard source location for the requirements JSON files and the requirements Python executable.

This blueprint defines the default location for a job-wide requirements file in the same directory of the test files or test-specific requirements files into a requirements directory preceded by the test file name. It is also possible to use sub-directories with the name of a specific test to define requirements for that test.

The following file tree is an example of possible use for requirements directories for a test:

├── CAbort.test_2
│   └── requirements.py
└── requirements.json

In this case, all the tests on cabort.py, except for CAbort.test_2, use the requirements.json file located at cabort.py.requirements. The CAbort.test_2 test uses its own requirements.py located at CAbort.test_2 directory inside the requirements directory. The tests located at the same directory of cabort.py use the requirements.json in the root directory.

Requirements files command-line parameter

It is also possible to use a command-line parameter to define the location of the requirements file. The command-line parameter supersedes all the other possible uses of requirements files. For that, this blueprint defines the parameter –requirements-file followed by the location of the requirements file. As a command-line example, we have:

avocado --requirements-file requirements.json run passtest.py


New Issue: Add the support for –requirements-file command-line parameter.

Backward Compatibility

The implementation of the requirements resolver, proposed here, affects Avocado’s behavior related to the tasks executed before a test execution starts.

To make the requirements resolver as flexible as possible, the implementation of this blueprint may change the utility APIs related to a requirement type.

Security Implications

Avocado users should have the warranty that their jobs are running in an isolated environment, but Avocado can, conservatively, create mechanisms to protect the users from running unintended code.

The use of a Python executable to build the requirements file is subject to security considerations. A malicious code distributed as a Python executable to build the requirements file can lead to security implications. This blueprint proposes a security flag in a general Avocado configuration file to avoid Python executable code to run by default. Users can change this flag anytime to be able to use the ability to run Python executable codes to generate the requirements JSON file.

Following is an example of how this flag can look like:

# Whether to run Python executables to build the requirements file
unsafe = False


New Issue: Add the unsafe flag support for the requirements resolver.

How to Teach This

We should provide a complete and detailed explanation of how to handle test requirements in the User’s Documentation.


New Issue: Create a complete section in the User’s Guide on how to handle test requirements.

Also, we should address how to create utility modules to handle new types of requirements in the Contributor’s Guide.


New Issue: Create a new section in the Contributor’s Guide on how to develop modules to handle new types of requirements.


[1] - https://lists.gnu.org/archive/html/qemu-devel/2019-11/msg04074.html

[2] - https://docs.python.org/3/library/json.html