Adding code necessitates an increase in the number of tests to guarantee the seamless functioning of the new code, potentially leading to a burdensome situation. Over time, testers may become discouraged, resulting in reduced testing efforts and an increased risk of defective code. Data-driven tests offer a solution to prevent this scenario. This article delves into the significance of data-driven tests and outlines the steps to implement them using the most effective mobile app testing tools.
What Is Data-Driven Testing?
Consider a situation where you must automate a test for an application featuring multiple input fields. Typically, in one-time or isolated cases, you might hardcode these inputs and execute the test. However, relying on hard coded inputs becomes impractical as your testing requirements expand. Hardcoding input values will rapidly lead to complexities, confusion, and an unmanageable situation when you must run numerous variations of input values for various test scenarios, including best-case, worst-case, positive, and negative scenarios.
Instead, picture having all your test input data stored or documented within a single spreadsheet. With this approach, you can instruct the test to “read” input values directly from the spreadsheet. This is precisely the objective of data-driven testing (DDT).
DDT differentiates the test logic (script) from the test data (input values), making both aspects more straightforward to create, modify, employ, and handle efficiently on a large scale. In essence, DDT is a methodology in which a sequence of test steps, structured as reusable test logic within test scripts, is automated to run repeatedly with different data sets obtained from various sources. This facilitates the comparison of actual and expected results for validation purposes.
Parameterized testing using mobile app testing tools is a procedural sequence consisting of four key steps:
- Acquiring input data from various sources such as .xls, .csv, .xml files, or other databases.
- Injecting the collected input data into the Application Under Test (AUT) using automated test scripts and variables.
- Scrutinizing the actual results against the expected output to validate the test.
- Repeating the identical test process with the subsequent row of data from the same source.
Advantages of Data-Driven Testing
Data-driven testing offers several noteworthy advantages:
- Data-driven testing expedites the execution of regression testing for web product features, allowing for the verification of end-to-end workflow logic with various values sourced from external data repositories.
- Data-driven tests are more straightforward to maintain due to the logical separation of test logic and the data used for testing. This separation ensures that minor changes in business rules might only necessitate the creation of new scenarios and additional data sets for test verification.
- Data-driven tests serve as a valuable tool for documentation by enabling the storage of test execution status alongside the input values used in the automated testing process.
- Data-driven testing is preferred for iteratively testing an application or web product against an extensive data set. This data set can encompass input values that encompass both positive and negative test scenarios, leading to enhanced test efficiency and coverage.
Data Driven Framework in Selenium WebDriver
The Data-Driven Framework stands out as an exceptionally effective and widely adopted Selenium Automation testing framework designed to facilitate iterative development and testing. This framework aligns with the principles of data-driven testing, enabling the execution of test cases and test suites by harnessing external data sources, such as Excel Sheets (xls, xlsx), CSV files (CSV), and similar formats. Establishing a connection with these external data sources empowers test scripts to seamlessly manipulate test data, resulting in efficient and precise testing.
When employing the Data-Driven Framework in conjunction with Selenium WebDriver, a clear distinction is made between the test data set and the test implementation. This separation significantly reduces the effort required to maintain and update the test code. Minor alterations in business rules necessitate adjustments in the test data set with minimal or no changes to the test code.
While Selenium WebDriver excels in automating cross-browser testing for web applications, it lacks the built-in capability to perform create, read, update, and delete (CRUD) operations on external data sources like Excel sheets, CSV files, and others. To address this limitation, third-party APIs like Apache POI come into play, enabling access to and executing relevant operations on these external data sources.
How to implement data-driven testing
Data-driven testing distinguishes itself from conventional Selenium Automation in its approach. All necessary data is embedded within the test script in a typical test. However, the test is linked to an external data source in data-driven testing. Various data sources can be employed, including uncomplicated CSV files to XML files and even comprehensive databases like MySQL.
Choosing your data source
In straightforward situations, a straightforward, structured text or CSV file suffices. This approach is suitable for scenarios where you aim to assess the login process using a series of username and password combinations. XML may be necessary for more intricate tests that require additional information in the test data. In extensive automated test suites, utilizing a robust relational database such as MySQL becomes essential, especially when coordinating and managing your tests effectively.
Connecting the data source
Once you have an external data source, integrating it with your test becomes a straightforward task when working with Selenium. For instance, if you are authoring a Selenium test in Python, you can easily incorporate a step to import your test data from a CSV file. Then, you can establish a loop that iterates through each entry in the data source.
In cases where your scenarios are more intricate, opting for XML as the data source may be a better choice. Similarly, you can import the XML data into your script and parse it to extract the data and the expected results. However, the complexity escalates when you need to utilize a database as your data source.
Analyzing the result
A significant hurdle lies in the analysis of test results. While some scenarios offer only a binary choice, others present multiple potential outcomes. To address this, a viable approach involves employing a case statement to compare the actual result with the anticipated one.
In cases where the result’s variability is higher, opting for XML can be advantageous to offer a more detailed description of the expected outcome. Ultimately, this is where the expertise of your test automation engineers comes into play, as they showcase their skill in handling these complexities.
By integrating data-driven testing with Selenium, testers can enhance the efficiency of their test cases. This optimization reduces timelines, simplifies their workflow, and culminates in a more comprehensive and higher-quality application evaluation.