robotframework-datadriver 0.3.6

Excel Support

For file support of xls or xlsx file you need to install the extra XLS or the dependencies. It contains the dependencies of pandas, numpy and xlrd. Just add [XLS] to your installation. New since version 3.6.

pip install --upgrade robotframework-datadriver[XLS]

Python 2

or if you have Python 2 and 3 installed in parallel you may use

pip3 install --upgrade robotframework-datadriver

DataDriver in compatible with Python 2.7 only in Version 0.2.7.

pip install --upgrade robotframework-datadriver==0.2.7

Because Python 2.7 is deprecated, there are no new feature to python 2.7 compatible version.

Options

*** Settings ***
Library    DataDriver
...    file=${None}
...    encoding=cp1252
...    dialect=Excel-EU
...    delimiter=;
...    quotechar="
...    escapechar=\\\\
...    doublequote=True
...    skipinitialspace=False
...    lineterminator=\\r\\n
...    sheet_name=0
...    reader_class=${None}
...    file_search_strategy=PATH
...    file_regex=(?i)(.*?)(\\.csv)
...    include=${None}
...    exclude=${None}

delimiter = ','
quotechar = '"'
doublequote = True
skipinitialspace = False
lineterminator = '\\r\\n'
quoting = QUOTE_MINIMAL

*** Settings ***
Library    DataDriver    my_data_file.csv    dialect=excel    encoding=${None}

delimiter = '\\t'
quotechar = '"'
doublequote = True
skipinitialspace = False
lineterminator = '\\r\\n'
quoting = QUOTE_MINIMAL

*** Settings ***
Library    DataDriver    my_data_file.csv    dialect=excel_tab

delimiter = ','
quotechar = '"'
doublequote = True
skipinitialspace = False
lineterminator = '\\n'
quoting = QUOTE_ALL

*** Settings ***
Library    DataDriver    my_data_file.csv    dialect=unix_dialect

*** Settings ***
Library    DataDriver    my_data_file.csv
...    dialect=UserDefined
...    delimiter=.
...    lineterminator=\\n

Limitation

How to activate the Data Driver

To activate the DataDriver for a test suite (one specific *.robot file) just import it as a library. You may also specify some options if the default parameters do not fit your needs.

Example:

*** Settings ***
Library          DataDriver
Test Template    Invalid Logins

Requirements

In the Moment there are some requirements how a test suite must be structured so that the DataDriver can get all the information it needs.

• only the first test case will be used as a template. All other test cases will be deleted.

• Test cases have to be defined with a Test Template. Reason for this is, that the DataDriver needs to know the names of the test case arguments. Test cases do not have named arguments. Keywords do.

• The keyword which is used as Test Template must be defined within the test suite (in the same *.robot file). If the keyword which is used as Test Template is defined in a Resource the DataDriver has no access to its arguments names.

Example Test Suite

***Settings***
Library           DataDriver
Resource          login_resources.robot
Suite Setup       Open my Browser
Suite Teardown    Close Browsers
Test Setup        Open Login Page
Test Template     Invalid Login

*** Test Case ***
Login with user ${username} and password ${password}    Default    UserData

***** *Keywords* *****
Invalid login
    [Arguments]    ${username}    ${password}
    Input username    ${username}
    Input pwd    ${password}
    click login button
    Error page should be visible

In this example, the DataDriver is activated by using it as a Library. It is used with default settings. As Test Template the keyword Invalid Login is used. This keyword has two arguments. Argument names are ${username} and ${password}. These names have to be in the CSV file as column header. The test case has two variable names included in its name, which does not have any functionality in Robot Framework. However, the Data Driver will use the test case name as a template name and replaces the variables with the specific value of the single generated test case. This template test will only be used as a template. The specified data Default and UserData would only be used if no CSV file has been found.

min. required columns

• *** Test Cases *** column has to be the first one.

• Argument columns: For each argument of the Test Template keyword one column must be existing in the data file as data source. The name of this column must match the variable name and syntax.

optional columns

• [Tags] column may be used to add specific tags to a test case. Tags may be comma separated.

• [Documentation] column may be used to add specific test case documentation.

Example Data file

In this data file, eight test cases are defined. Each line specifies one test case. The first two test cases have specific names. The other six test cases will generate names based on template test cases name with the replacement of variables in this name. The order of columns is irrelevant except the first column, *** Test Cases ***

CSV / TSV (Character-separated values)

By default DataDriver reads csv files. With the Encoding and CSV Dialect settings you may configure which structure your data source has.

XLS / XLSX Files

If you want to use Excel based data sources, you may just set the file to the extention or you may point to the correct file. If the extention is “.xls” or “.xlsx” DataDriver will interpret it as Excel file. You may select the sheet which will be read by the option sheet_name. By default it is set to 0 which will be the first table sheet. You may use sheet index (0 is first sheet) or sheet name(case sensitive). XLS interpreter will ignore all other options like encoding, delimiters etc.

*** Settings ***
Library    DataDriver    .xlsx

or:

*** Settings ***
Library    DataDriver    file=my_data_source.xlsx    sheet_name=2nd Sheet

PICT (Pairwise Independent Combinatorial Testing)

Pict is able to generate data files based on a model file. https://github.com/Microsoft/pict

Documentation: https://github.com/Microsoft/pict/blob/master/doc/pict.md

*** Settings ***
Library    DataDriver    my_model_file.pict

file=

*** Settings ***
Library         DataDriver    file=../data/my_data_source.csv

• None(default): Data Driver will search in the test suites folder if a *.csv file with the same name than the test suite *.robot file exists

• only file extention: if you just set a file extentions like “.xls” or “.xlsx” DataDriver will search

• absolute path: If an absolute path to a file is set, DataDriver tries to find and open the given data file.

• relative path: If the option does not point to a data file as an absolute path, Data Driver tries to find a data file relative to the folder where the test suite is located.

encoding=

may set the encoding of the CSV file. i.e. cp1252, ascii, iso-8859-1, latin-1, utf_8, utf_16, utf_16_be, utf_16_le, etc… https://docs.python.org/3.7/library/codecs.html#standard-encodings

dialect=

You may change the CSV Dialect here. If the Dialect is set to ‘UserDefined’ the following options are used. Otherwise, they are ignored. supported Dialects are:

"excel"
    delimiter = ','
    quotechar = '"'
    doublequote = True
    skipinitialspace = False
    lineterminator = '\\r\\n'
    quoting = QUOTE_MINIMAL

"excel-tab"
    delimiter = '\\t'

"unix"
    delimiter = ','
    quotechar = '"'
    doublequote = True
    skipinitialspace = False
    lineterminator = '\\n'
    quoting = QUOTE_ALL

Defaults:

file=None,
encoding='cp1252',
dialect='Excel-EU',
delimiter=';',
quotechar='"',
escapechar='\\\\',
doublequote=True,
skipinitialspace=False,
lineterminator='\\r\\n',
sheet_name=0

Using Custom DataReader

DataReader classes are loaded dynamically into DataDriver while runtime. DataDriver identifies the DataReader to load by the file extantion of the data file or by the option reader_class.

*** Settings ***
Library    DataDriver    file=mydata.csv

*** Settings ***
    Library    DataDriver   file=mydata.csv    reader_class=generic_csv_reader    dialect=userdefined   delimiter=\\t    encoding=UTF-8

Create Custom Reader

Recommendation:

Have a look to the Source Code of existing DataReader like csv_reader.py or generic_csv_reader.py .

To write your own reader, create a class inherited from AbstractReaderClass.

Your class will get all available configs from DataDriver as an object of ReaderConfig on __init__.

DataDriver will call the method get_data_from_source This method should then load your data from your custom source and stores them into list of object of TestCaseData. This List of TestCaseData will be returned to DataDriver.

AbstractReaderClass has also some optional helper methods that may be useful.

You can either place the custom reader with the others in DataDriver folder or anywhere on the disk. In the first case or if your custom reader is in python path just use it like the others by name:

*** Settings ***
Library          DataDriver    reader_class=my_reader

In case it is somewhere on the disk, it is possible to use an absolute or relative path to a custom Reader. Imports of custom readers follow the same rules like importing Robot Framework libraries. Path can be relative to ${EXECDIR} or to DataDriver/__init__.py:

*** Settings ***
Library          DataDriver    reader_class=C:/data/my_reader.py    # set custom reader
...                            file_search_strategy=None            # set DataDriver to not check file
...                            min=0                                # kwargs arguments for custom reader
...                            max=62

This my_reader.py should implement a class inherited from AbstractReaderClass that is named my_reader.

from DataDriver.AbstractReaderClass import AbstractReaderClass  # inherit class from AbstractReaderClass
from DataDriver.ReaderConfig import TestCaseData  # return list of TestCaseData to DataDriver


class my_reader(AbstractReaderClass):

    def get_data_from_source(self):  # This method will be called from DataDriver to get the TestCaseData list.
        test_data = []
        for i in range(int(self.kwargs['min']), int(self.kwargs['max'])):  # Dummy code to just generate some data
            args = {'${var_1}': str(i), '${var_2}': str(i)}  # args is a dictionary. Variable name is the key, value is value.
            test_data.append(TestCaseData(f'test {i}', args, ['tag']))  # add a TestCaseData object to the list of tests.
        return test_data  # return the list of TestCaseData to DataDriver

See other readers as example.

Selection of test cases by name

robot --variable "DYNAMICTEST:my suite name.test case to be executed" my_suite_name.robot

robot --variable DYNAMICTESTS:firstsuitename.testcase1|firstsuitename.testcase3|anothersuitename.othertestcase foldername

Re-run failed test cases:

Because it is not possible to use the command line argument --rerunfailed from robot directly, DataDriver brings a Pre-Run-Modifier that handles this issue.

Normally reexecution of failed testcases has three steps.

• original execution

• re-execution the failed ones based on original execution output

• merging original execution output with re-execution output

The DataDriver.rerunfailed Pre-Run-Modifier removes all passed test cases based on a former output.xml.

Example:

robot --output original.xml tests                                                    # first execute all tests
robot --prerunmodifier DataDriver.rerunfailed:original.xml --output rerun.xml tests  # then re-execute failing
rebot --merge original.xml rerun.xml                                                 # finally merge results

Be aware, that in this case it is not allowed to use “:” as character in the original output file path. If you want to set a full path on windows like e:\\myrobottest\\output.xml you have to use “;” as argument seperator.

Example:

robot --prerunmodifier DataDriver.rerunfailed;e:\\myrobottest\\output.xml --output e:\\myrobottest\\rerun.xml tests

Filtering with tags.

New in 0.3.1

It is possible to use tags to filter the data source. To use this, tags must be assigned to the test cases in data source.

*** Settings ***
Library    DataDriver    include=1OR2    exclude=foo