Provar's Data-Driven Testing: Unlocking New Possibilities

You can’t scale Salesforce testing by just adding in more test scripts. You scale by making every test smarter, reusable and data-powered.

In this blog, we will explore how Provar’s Data-Driven Testing approach enhances test automation by enabling testers to execute the same test case with multiple data sets effortlessly. You'll learn how this feature helps improve test coverage, reduces manual effort, and ensures the reliability of Salesforce applications.

What is Data-Driven Testing (DDT)?

Data-driven testing (DDT) is a testing methodology where test scripts are executed with different sets of data to validate the functionality of an application under various conditions. Instead of writing multiple test cases for different data values, DDT allows the same test case to be executed with various inputs, making it highly efficient and scalable. The test data is typically stored externally, in formats like CSV files, Excel spreadsheets, JSON, Salesforce applications, or even APIs, and it is fed into the test script at runtime.

In this approach, test logic remains the same, but the input data changes. This allows testing the same functionality with different sets of data, including positive, negative, boundary, and edge cases.

Why Do We Need Data-Driven Testing?

• Increases Test Coverage
  
  • Allows testing multiple scenarios with different data sets.
  • Ensures comprehensive coverage by testing positive, negative, and boundary cases.
• Promotes Reusability and Efficiency
  
  • Reduces code duplication by reusing the same test scripts with different data sets.
  • Saves time and effort by executing the same test logic with various data inputs.
• Improves Test Maintainability
  
  • Separates test data from test logic, simplifying maintenance.
  • Easily update test data without rewriting test cases.
• Reduces Costs
  
  • Decreases redundancy, minimizing the overall effort for testing.
  • Reduces manual testing costs by automating repetitive tests.
• Supports Scalability
  
  • Easily handles large data sets as your application grows.
  • Adapts quickly to new features and functionality by updating data instead of test scripts.
• Identifies Edge Cases and Bugs
  
  • Helps identify hidden bugs by testing under various conditions, including edge cases.
  • Detects unexpected failures from specific data values or conditions.
• Generates Better Test Reporting
  
  • Produces detailed reports showing how each data set performed.
  • Helps identify which data combinations caused failures, aiding in accurate debugging.

Impact of Not Using Data-Driven Testing in Automation

When data-driven testing is not used in automation testing, several challenges can arise, particularly around efficiency, scalability, and coverage. Here's an overview of the key challenges that automation teams may face:

• Limited Test Coverage - Tests are confined to a narrow range of data inputs, leading to missed edge cases and insufficient scenario coverage.

• Increased Maintenance Effort - Test scripts need manual updates for each data change, making maintenance time-consuming and error-prone.

• Scalability Issues - Writing separate tests for each data set becomes impractical as data grows, making scaling difficult.

• Redundant Test Scripts - Duplicated test code for similar functionality increases complexity and maintenance effort.

• Longer Test Execution - Tests must be run sequentially for each input set, slowing down feedback and increasing testing time.

• Difficulty Handling Dynamic Data - Changes in data or external sources require manual updates to each test, leading to inconsistency and a higher risk of errors.

• Increased Costs - More time and resources are needed for writing, executing, and maintaining tests, raising overall testing costs.

Implementing Data-Driven Test Automation with Provar

In today’s fast-paced business environment, ensuring the seamless functionality of Salesforce applications is crucial for smooth operations. Provar for Salesforce is a no-code, intelligent test automation tool specifically designed for Salesforce environments, providing a powerful solution for automated testing. By using data-driven testing, Provar allows organizations to easily test various data inputs and scenarios, ensuring flawless performance across different use cases and data sets. Its robust capabilities empower users to automate tests without complex scripting, execute tests with diverse data sets, improve test coverage, and reduce manual effort. This makes Provar a game-changer for Salesforce testing, especially in the era of data-driven testing.

Provar supports various types of data-driven testing that allow flexibility in how test data is integrated, managed, and used.

Here are the key data-driven testing types in Provar:

1. CSV/Excel/JSON Data-Driven Testing

Provar supports a variety of data formats for data-driven testing, including CSV, Excel, and JSON. These formats allow you to supply test data that can be used across multiple test cases, enabling automation of tests with various input values without the need for manual intervention. Below is a detailed guide on how to implement CSV, Excel, and JSON-based data-driven testing in Provar.

Let’s take an example of Lead Creation with different types of data sets.

Let’s take an example of Lead Creation with different types of data sets.

1. Prepare Test Data File

• Your test data should be in a CSV, Excel, or JSON file, with rows and columns.
• The column headers should represent test parameters (e.g., Lead Source, First Name, Last Name, etc.).

2. Save the File

• Save your file on your local machine, preferably in the templates folder in your workspace.

3. Add Parameter Value Source

• To read the file into your test case, Add Set Values step from the Test Palette and click the Add Parameter Value Source icon ( Ⓧ ) in the top-right of the Test Step.

4. Populate the Test Step

• For Windows: Highlight the data range in your Excel file, press CTRL+C to copy, then click the Populate From File icon in Provar to load the file.
• For Mac: Manually fill in the File, Worksheet, and Values Range fields using the File Chooser icon to select the file path.

5. Configure the Test Step

• In the Name field, choose how the headers should be organized (orientation).
• In the Where field, set any filters for the data you want to import.
• In the Variable field, define a name for the data (default is ExcelData).

6. Parameterize the Test Case

• Use the For Each step to loop through rows in the spreadsheet, allowing the same actions to be performed for each row.
• Drag the For Each step from the Test Palette to your test.

7. Configure For Each Step

• In the List field, add SourceData (this is the data source).
• In the From Item field, specify the starting row (first record).
• In the Value Name field, name the row data (e.g., Row).
• Enable Continue on Failure to keep looping through rows even if a failure occurs.

8. Repeat Test Steps

• Add the steps you want to repeat inside the For Each loop.
• Make sure to select and move all the steps together to maintain order.

9. Update Test Steps

• In the test steps, use the Row variable (from the For Each loop) to refer to specific data fields.
• Use dot notation (e.g., Row.FieldName) to link the data to the test steps.

10. Run the Test

• The test will now loop through all the rows in the spreadsheet and execute the steps for each set of data.

11. Data Source Type

• If you're using a CSV file, select CSV File as the source.
• If you're using a JSON file, select JSON File as the source.

2. Database Data-Driven Testing

With this feature, you can pull data directly from databases such as Oracle, PostgreSQL, MySQL, SQL Server, or DB2 to drive your test cases, ensuring your Salesforce implementation integrates correctly with other systems and databases.

Provar can connect to a variety of database management systems (DBMS) to retrieve data for test execution.

Example: When a new Account is created in Salesforce, it should create the corresponding Customer record in NetSuite.

• Create an Excel, CSV, or JSON file with the data needed for account creation as a Pre-Requisite.
• Create an Oracle Database connection to the NetSuite database as a Pre-Requisite.

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• Use the data from the Excel/CSV/JSON file to automate the account creation process in the UI.
• Create the account record in Salesforce using the data from the Excel/CSV/JSON file.
• Run an Apex job to push the data from Salesforce to NetSuite.
• Write an Oracle query with conditions to fetch the customer record and store the result in a variable.

• Use ‘Assert’ steps to compare the data between Salesforce and NetSuite applications.

3. API Data-Driven Testing

Provar can directly pull data from Salesforce objects (i.e. Accounts, Contacts, Opportunities, etc.) and use them dynamically in your test cases. This allows testing with live Salesforce data which enhances the flexibility and robustness of your Salesforce testing, ensuring that your application works seamlessly with real-world data.

Let’s see how to use the existing Account record and Contact record to create an Opportunity.

• Write a SOQL to fetch the Account with conditions and store it in a variable (i.e. AccountRows):
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  SELECT ID, Name FROM Account WHERE Name = 'Test Account' AND Industry = 'Education' AND Type = 'Reseller' AND status = 'Operational' LIMIT 1

• Write a SOQL to fetch the Contact with conditions and store it in a variable (i.e. contactRows):
  ‍
  SELECT Id, FirstName, LastName, Email FROM Contact WHERE AccountId = {$AccountRows.ID} AND status = 'Active' LIMIT 1

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• Automate the UI steps, feeding the Account Name and Contact Name into the lookup fields to create and submit the Opportunity.

4. Dynamic Data-Driven Testing Using functions

Provar enables the use of dynamic data during test execution, which is particularly beneficial when you need to generate random values or retrieve data dynamically throughout the test process. You can define dynamic variables that are populated as the test runs, such as generating random text for use within a test case.

Here are some of the key functions in Provar that help generate random data:

UniqueId

The UniqueId function generates a unique value, commonly used in a "Set Values" test step to create a variable. This variable can then be referenced to generate and identify test data throughout the test.

• {UniqueId(8)} → Generates a unique ID of length 8 with alphanumeric characters, e.g., I67OOO54.
• {UniqueId(12, NUMERIC)} → Generates a unique ID of length 12 using only numbers, e.g., 412373164167.
• {UniqueId(12, ALPHA)} → Generates a unique ID of length 12 using letters only, e.g., GTWAWNZYWSIC.
• {UniqueId(36, UUID)} → Generates a universally unique ID of length 36, e.g., 8398e3c6-94ef-4e90-a809-03191424fda0.
  

Example:

• Generating a Random Alphanumeric Name for Account Name.

DateAdd

The DateAdd function allows you to add or subtract a specific number of days from a given date, creating a future or past date. This is particularly useful for avoiding hardcoded date values in test steps, such as preventing a test failure due to an outdated Close Date.

• {TODAY} → Returns the current date in the GMT zone, e.g., 2015-01-16.
• {NOW} → Returns the current date and timestamp in the GMT zone, e.g., 2015-01-16 10:13:43.0.
• {DateAdd(TODAY, 1)} → Adds one day to the current date, e.g., 2015-01-17.
• {DateAdd(TODAY, "-1")} → Subtracts one day from the current date, e.g., 2015-01-15.
• {DateAdd(TODAY, 1, MONTH)} → Adds one month to the current date, e.g., 2015-02-16.
• {DateAdd(NOW, "-2", HOUR)} → Subtracts two hours from the current date and time, e.g., 2015-01-16 08:15:27.0.
  Note: When using negative numbers, enclose the value in double quotes, e.g., {DateAdd(TODAY,"-1")}.
  

Examples:

• Adding 1 Day to the Current Date for an Opportunity Close Date
• Subtracting 1 Day from the Current Date for a Past Task Due Date
• Adding 1 Month to the Current Date for Subscription Renewal Date.

DateFormat

The DateFormat function is used to convert a date into a specific format.

• {DateFormat()} → Defaults to the system date in the format yyyy-MM-dd, e.g., 2015-01-16.
• {DateFormat(TODAY, "MM-dd-YYYY")} → Modifies the date format to display the month first, e.g., 01-16-2015.
• {DateFormat(DateAdd(TODAY, 5), "MM-dd-YYYY")} → Applies to other functions, e.g., 01-21-2015.
• {DateFormat(NOW, "MM-dd-YYYY hh a zzzz", "GMT")} → Includes a timezone in the format 01-16-2015 10 AM Greenwich Mean Time.
  

Examples:

• Formatting Today's Date in MM-DD-YYYY Format
• Formatting a Date After Adding 5 Days
• Including Timezone in Date Formatting for Logging or Reporting

DateParse

The DateParse function is used to convert a string into a date, following a specified input and output format.

• {DateParse("2015-03-15", "yyyy-MM-dd")} → Converts the string 2015-03-15 into a 2015-03-15.
• {DateParse("2015-03-15", "yyyy-MM-dd", "MM-dd-yyyy")} → Converts the string into a date and formats it to a new format, e.g., 03-15-2015.

Example:

• Parse a date string in yyyy-MM-dd format and convert it into a Salesforce Date field

These functions provide flexibility when working with dynamic data in Provar, making your tests more robust and adaptable to varying conditions.

Conclusion:

Provar’s data-driven testing capabilities make it possible to run the same test with multiple sets of data — whether sourced from external files (CSV/Excel/JSON), Salesforce objects, APIs, or dynamically generated values. This approach enhances test coverage, reduces duplication, and ensures Salesforce applications perform seamlessly across a wide variety of real-world scenarios.

But the true power of Provar is unlocked when paired with the right strategy and expertise. At Cloud Peritus, we don’t just implement tools — we design intelligent, scalable automation frameworks that align with your business goals. Our proven Salesforce and Provar expertise enables organizations to transform repetitive testing into a streamlined, cost-efficient, and future-ready automation strategy.

With our spotless track record and customer-first approach, we ensure that Provar isn’t just another tool in your stack, but a catalyst for long-term quality, speed, and confidence in your Salesforce ecosystem.

Ready to see how Cloud Peritus can help you scale testing excellence with Provar? Reach out now to avail our 5 year anniversary success guarantee offer HERE (Valid till 30th Sep 25) or email us at info@cloudperitus.com to schedule a quick consultation with no strings attached.