Robot Framework SchemathesisLibrary
Robot Framework SchemathesisLibrary is a library build top of the Schemathesis. Schemathesis automatically generates thousands of test cases from your OpenAPI or GraphQL schema and finds edge cases that break your API.
SchemathesisLibrary uses DataDriver to create test cases from the Schemathesis Case object.
Installation
Install with pip, uv or any package manager that supports PyPi
pip install robotframework-schemathesislibrary
Keyword documentation
See keyword documentation for more details. A link older keyword documentation can be found from versions page
Usage
Test are automatically generated based your API specification, SchemathesisLibrary uses DataDriver internally, but you need to create template suite, so that DataDriver is able to create needed test for your test suite.
SchemathesisLibrary must be imported by url or path argument, which tell where
the API specification can obtained. As like with Datadriver, there must be
Test Template
setting defined. The template keyword must take one argument, usually referred as
${case} and the template keyword must call Call And Validate keyword with the
${case} argument.
Example test suite:
*** Settings ***
Library SchemathesisLibrary url=http://127.0.0.1/openapi.json
Test Template Wrapper
*** Test Cases ***
All Tests # This test is deleted by DataDriver
Wrapper test_case_1
*** Keywords ***
Wrapper
[Arguments] ${case}
Call And Validate ${case}
Strict mode
Not every operation in a schema can be turned into test cases. Schemathesis skips an operation it can not parse, for example one that refers to a component that does not exist in the schema, and that operation is never tested.
By default the library raises an error that names every operation it could not parse,
and the suite does not run. Import the library with strict=False to skip those
operations instead, test the rest of the schema, and get a warning in the log that names
what was skipped:
*** Settings ***
Library SchemathesisLibrary url=http://127.0.0.1/openapi.json strict=False
Use strict=False knowingly. The skipped operations are not tested, the suite still
passes, and the only sign that your coverage shrank is a warning in the log.
Regardless of strict, the library always raises an error when the schema produces no
test cases at all, because a suite without test cases passes without testing anything.
Authentication
Dynamic token authentication
Library currently supports Schemathesis
dynamic token
authentication by the library import auth argument. The dynamic token generation
class should follow the Schemathesis documentation. The only addition is the import.
Importing the class must follow the Robot Framework library
import rules
, example if importing with filename, filename much match to the class name. Example
if test case looks like:
*** Settings ***
Library SchemathesisLibrary url=http://127.0.0.1/openapi.json auth=${CURDIR}/AuthExtension.py
Test Template Wrapper
*** Test Cases ***
All Tests
Wrapper test_case_1
*** Keywords ***
Wrapper
[Arguments] ${case}
Call And Validate ${case}
And AuthExtension.py looks like
from base64 import b64encode
import schemathesis
from robot.api import logger
@schemathesis.auth()
class AuthExtension:
def get(self, case, ctx):
# Instead of hard coding secrets to class, it is better to get them dynamically.
# Jenkins or GitHub secrets, Azure keyvault, or from somewhere which is appropriate
# for your needs.
return b64encode("joulu:pukki".encode("utf-8")).decode("ascii")
def set(self, case, data, ctx):
case.headers = case.headers or {}
case.headers["Authorization"] = f"Basic {data}"
logger.debug(f"Updated headers for case: {case.operation.method} {case.operation.path}")
Then with all API calls, will have basic auth set in the headers for all calls made to your API endpoint.
Schemathesis hook support
Library supports extending Schemathesis by defining hooks. Hooks allows users to customize how Schemathesis generates test data, validates responses, and handles requests through hooks, custom checks, and data generation strategies. For more details about Schemathesis hooks, refer to Schemathesis extending documentation: https://schemathesis.readthedocs.io/en/stable/guides/extending/
Example if there need to add custom header in each request, then it is possible to import library with hook:
*** Settings ***
Variables authentication.py
Library SchemathesisLibrary
... url=http://127.0.0.1/openapi.json
... hook=${CURDIR}/hook_filter.py
Test Template Wrapper
*** Test Cases ***
All Tests
Wrapper test_case_1
*** Keywords ***
Wrapper
[Arguments] ${case}
Call And Validate ${case} auth=${BASIC_AUTH_TUPLE}
And when hook_filter.py looks like:
import schemathesis
global_count_count = 0
@schemathesis.hook
def filter_query(ctx, query) -> bool:
method = ctx.operation.method.lower().strip()
if method == "put":
global global_count_count
global_count_count += 1
if global_count_count > 2:
return False
return True
Then only two test with PUT request are generated.
Pabot Support
Pabot is a parallel executor for Robot Framework tests, and
SchemathesisLibrary supports it.
However, there's a crucial point to consider. Because SchemathesisLibrary uses
DataDriver to generate
tests dynamically, you cannot use Schemathesis's built-in parallel execution
feature (--workers option).
When using Pabot, you are responsible for organizing your test suites to avoid generating the same tests in multiple parallel processes. A good strategy might be to split your test into different suites, where each suite targets a specific scenario.
For example, you could create separate suites for:
- A user that is not found in the system.
- A user that is found and has valid credentials.
- A user that is found but has invalid credentials.
This approach ensures that each Pabot process works on a unique set of tests. Since every API and system is different, a universal rule for structuring suites is not possible, but this scenario-based division might be good starting point.
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