Test Data Management Strategies for Automation Testing

Test Data Management Strategies for Automation Testing

Efficient test data management is vital for automation testing, but it has challenges. Here are strategies to overcome them.

Example: Test Data Management Strategies in Action

// Example 1: Data Masking
Mask sensitive data in test environments to ensure compliance with privacy regulations
Replace actual data with fictional but realistic values to maintain test realism and integrity

// Example 2: Test Data Generation
Automatically generate test data using tools like Mock Data Generator
Create diverse datasets to cover various scenarios and edge cases

Practical Exercises

• Implement data masking techniques to anonymize sensitive information in your testing.
• Utilize test data generation tools to automatically create diverse datasets for your automation tests.

FAQ (Interview Questions and Answers)

1. What are the challenges of test data management in automation testing?
   There are no challenges in test data management.
   Challenges include ensuring data privacy, maintaining data integrity, and generating diverse datasets.
   Test data management is only relevant for database developers.
   
2. What is data masking in the context of test data management?
   Data masking is a technique for creating diverse datasets.
   Data masking is not used in test data management.
   Data masking involves obscuring sensitive information in test environments to comply with privacy regulations.
   
3. How can you ensure data integrity in test data management?
   By maintaining the consistency and accuracy of test data throughout the testing process.
   Data integrity is not relevant in test data management.
   Data integrity refers to the security of test data.
   
4. What is the benefit of using test data generation tools?
   Test data generation tools are not useful in automation testing.
   Test data generation tools only create fictional data.
   Test data generation tools automate the process of creating diverse datasets, saving time and effort in test data preparation.
   

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Remember to just comment if you have any doubts or queries.

Mock Data Generator (QA Tools) Next-Generation - test data generation tools

Test data generator in software testing generates many types of mock data, normal/ boundary/ erroneous data, to save you time and test rigorously.

Name UserName Password Email Handle Phone Street Address Postal/Zip Country Currency Date Product Menu Item Prompt Transaction Tag URL Service Endpoint IP Address

How to Use Mock Data Generator

The Mock Data Generator is a tool that can be used to generate realistic and meaningful mock data for testing software. The tool can be used to generate a variety of data types, including:

• Names
• UserNames
• Passwords
• Emails
• Handles
• Phone Numbers
• Street Addresses
• Postal/Zip Codes
• Countries
• And much more!

The Mock Data Generator can be used to generate normal, lower boundary, upper boundary and erroneous mock data for a variety of purposes, such as:

• Testing the functionality of a software application
• Generating data for training machine learning models
• Creating seed data for a database
• And many more!

To use the Mock Data Generator, simply visit Mock Data Generator web page.

On the website, you will see a dropdown where you can select the types of data you want to generate. In the dropdown, choose one or more option(s). Then click Preview Data. If you want to export mock data of the selected option(s), enter the number of records that you want to generate. Then click Export as CSV. The Mock Test Data Generator will then generate a CSV file containing the mock data. You can download the CSV file and use it to test your software application or train your machine learning model.

If you have any questions or issues using the Mock Test Data Generator, use the Contact Us form (on the right) or email the developer at isingh30@gmail.com please.

Test Data Management

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Test data management is the process of planning, designing, procurement, storing, maintaining, securing and using the data that is used for testing software. Test data management helps to ensure that the data used for software testing is accurate, relevant, and up-to-date, and it properly reflects the real-world conditions in which the software will run. Test data management also helps to improve the quality and efficiency of testing, to protect sensitive data from unauthorized access or misuse.

Test Data Management Examples

• A test data management tool for a web application allows testers to create, edit, delete, and assign test data sets, link test data sets to test cases, generate synthetic or masked test data, provision test data to test environments, and monitor test data usage and quality.
• A test data management tool for a desktop application allows testers to locally create, edit, delete, and assign test data sets, link test data sets to operating systems or the desktop application, generate synthetic or masked test data, provision test data to test environments, and monitor test data usage and quality.
• A test data management tool for a cloud-based application allow testers to create, edit, delete, and assign test data sets, link test data sets to cloud platforms or cloud services, generate synthetic or masked test data, provision test data to test environments, and monitor test data usage and quality.
• A test data management tool for a mobile app allows testers to create, edit, delete, and assign test data sets, link test data sets to mobile devices or emulators, generate synthetic or masked test data, provision test data to test environments, and monitor test data usage and quality.
  

Tips for Test Data Management

• Use best practices such as defining the test data requirements and scope, designing the test data architecture, creating baseline and reusable and maintainable test data sets, generating realistic and diverse test data, provisioning test data on demand and in sync with testing cycles.
• Use a test data management tool for version control, and to automate and streamline your test data management activities
• Use test data management techniques such as synthetic data generation, production cloning, production masking, production subsetting, self-service provisioning for creating high-fidelity and compliant test data.
• Use test data management measurements such as number of test data sets created, test cases linked to test data sets, defects related to test data, environments provisioned with test data, test data reuse, test data refreshes, sensitive fields masked, records subsetted to help you evaluate and improve your test data activities.

FAQ (interview questions and answers)

1. What is the purpose of test data management?
   Provide high-quality and compliant data for testing your software.
2. What are the benefits of test data management?
   It improves the variety, quality and efficiency of software testing. It reduces the need to design test data during test execution. It protects sensitive test data from unauthorized access or misuse.
3. What are the challenges of test data management?
   It requires initial investment in terms of cost, time, training, and implementation. The test data management tool may not be compatible with the type of software tested, or it may have limited features or functionality, and it may need regular maintenance and updates.
4. How to evaluate a test data management tool?
   Ease of use, features, version control, functionality, integration with test management tools, concurrent user support, security, customer support and pricing.

Remember to just comment if you have any doubts or queries.

SoapUI Data Driven Testing Groovy

Let us continue with SoapUI testing tutorials. This SoapUI tutorial for beginners is on SoapUI data driven testing with Groovy script. What is data driven testing? It means that you store the test data in some format e.g. in an XML file, an Excel sheet or a database and then use that test data in your tests. The advantage of data driven testing using SoapUI is that you can run your tests with multiple test data values. First view this SoapUI Data Driven Testing Groovy tutorial. Then continue reading.

SoapUI free version does not give the  user interface to create data driven tests. However, SoapUI Pro provides a DataSource test step to get test data from sources like XML files, Excel sheets, files, directories and databases. This test data can be put into SoapUI properties and used in test steps. Also, a DataSource Loop test step is available to loop the previous test steps for each row of test data in the data source.

Note: You can see how to do data driven testing in SoapUI free version in my tutorial on SoapUI Data Driven Testing Groovy.

We can write a Groovy script in Soap UI tool to get the test data and run our test steps. This is how I implemented data-driven testing using Groovy.

1. There is a library to handle Excel files using Java code, called JExcelApi. I downloaded it from SourceForge. Then I unzipped it. After unzip, the jxl.jar should be copied to the SoapUI lib folder (alternately, it can be copied to the SoapUI bin/ext folder). Then, I re-started Soap UI.
2. Next, I put my test data in an Excel file. In my case, there were two columns, one for Numbers and the other for the same number in words. I used each Number as a parameter of my test request. SoapUI load test data from file in Excel.
   
3. Then, I added a Properties test step. I clicked on the + icon to add properties. There were 5 properties. Number stored the parameter value. Word tested the assertion. Counter, Total and End properties were used in the Groovy script logic. Counter has to have an initial value of 0. End has to have an initial value of False.
   
4. Next, I added a Groovy script test step because we have to do data driven testing in SoapUI using groovy script.
   import jxl.* // import Java Excel API library
   def TestCase = context.testCase
   def FilePath = "E:\\Training\\SoapUI\\Files\\NumbersWords.xls"
   def count
   Workbook WorkBook1 = Workbook.getWorkbook(new File(FilePath))
   Sheet Sheet1 = WorkBook1.getSheet(0)
   PropertiesTestStep = TestCase.getTestStepByName("Properties")
   count = PropertiesTestStep.getPropertyValue("Counter").toInteger()
   //If Total records is unknown (at start), get the rowcount from Excel
   if (PropertiesTestStep.getPropertyValue("Total").toString() == "")
       PropertiesTestStep.setPropertyValue("Total", Sheet1.getRows().toString())
   count++
   //Read the Excel test data
   Cell Field1 = Sheet1.getCell(0, count)
   Cell Field2 = Sheet1.getCell(1, count)
   log.info ("Count is " + count.toString() + " Number : " + Field1.getContents() + " Word : " + Field2.getContents())
   WorkBook1.close()
   //Copy the Excel test data to properties in Properties test step
   PropertiesTestStep.setPropertyValue("Number", Field1.getContents())
   PropertiesTestStep.setPropertyValue("Word", Field2.getContents())
   PropertiesTestStep.setPropertyValue("Counter", count.toString())
   if (count == PropertiesTestStep.getPropertyValue("Total").toInteger() - 1)
       PropertiesTestStep.setPropertyValue("End", "True")
   
5. Also, I added a Groovy script test step to implement the data loop.
   def TestCase = context.testCase
   PropertiesTestStep = TestCase.getTestStepByName("Properties")
   Stop = PropertiesTestStep.getPropertyValue("End").toString()
   if (Stop=="True")
       log.info("Exit Groovy Script - DataLoop")
   else
       testRunner.gotoStepByName("Groovy Script")
6. One thing to keep in mind is that the test steps in the test case should have the Groovy script as the first step and Groovy script with data loop as the last step. 
   
7. In the request, I put a property expansion as the parameter value. This means that the request read the Number parameter value from the property, Number. In order to test the response, I put an assertion. This assertion also used a property expansion The assertion wa tested against the property, Word.
   
8. Ensured that the properties are initialized correctly. Then, I ran the test case. 
9. After the test case is run, in the Properties test step, Number and Word should have the last row data. Also, in the script log, each Number and Word should have been used.
10. I also had a cleanup step (disabled in the Step 6 image above) to reset the property values after each run of the test case.
    def TestCase = context.testCase
    PropertiesTestStep = TestCase.getTestStepByName("Properties")
    PropertiesTestStep.setPropertyValue("Number","")
    PropertiesTestStep.setPropertyValue("Word", "")
    PropertiesTestStep.setPropertyValue("Counter", "0")
    PropertiesTestStep.setPropertyValue("Total", "")
    PropertiesTestStep.setPropertyValue("End", "False")

This is how you can also do SoapUI data driven testing with Groovy script. If you want to see this complete Soap UI data driven testing example, it is available in my SoapUI data driven testing tutorial. Thank you.

Reference : https://www.anilvij.com/how-to-read-data-from-excel-using-groovy-soapui/

What is JSON

Every now and then, you must have heard the term, JSON. Let us learn JSON. The JSON full form is JavaScript Object Notation. It is a format for data exchange. Other data interchange formats are CSV and XML. There are several JSON data value types like String, Number, Object etc. View my 10-minute JSON tutorial or read more on JSON data examples...

JSON tutorial for beginners

JSON (JavaScript Object Notation) is a popular data format that you should know. In order to learn JSON, you can view my 10-minute JSON tutorial (with more details) or read on for a quick JSON introduction.

 

JSON data format is language-independent and an alternative to other data formats like CSV or XML. JSON can be understood by both humans and computer devices. Douglas Crockford created the JSON format. 

How to use application metadata for software test automation?

Metadata is commonly understood as data that describes data. An example of metadata is the product catalog of a company. Such a catalog contains additionally assigned attributes (i.e. related but extrinsic attributes) such as name, model, price and availability of products. Another example of metadata is the data within a meta tag of a web page. This metadata includes the description of the web page and keywords. This metadata is not used to generate the web page content but is used by search engines. Still another example of metadata is the details of a digital image such as title, width, height, resolution, camera used and so on. This metadata is used to search the image in an image library and also to modify the image as desired. In the same way, a deployed software application build has metadata.

Software test automation needs certain application metadata in order to execute. Such metadata data is perhaps the application URL or path to the executable file, valid users' credentials (user names and passwords) and their privilege levels, internal name of the application, application version, components released in the build, server addresses, application database name and application database user credentials. It is common for such data and more to be available in the application build release notes. It is also common for the release notes to be created using standard markups like HTML or XML. Now, if test automation is aware of the schema used in the release notes (i.e. which tag contains which data), it can query the release notes and extract the required data. Test automation can also store this data in the test database. This provides readily available data to run manual tests and also to generate test run reports. Auto population of application metadata in the database works well with automated software build systems which create regular application builds and the corresponding release notes. In such a case, test automation can wait for some notification or keep polling the network location for a new build. Whenever the new build is available, it can auto populate the application metadata and launch the build sanity test (or any other test per the automated test plan).

Until next time.

Image courtesy of fotographic1980 / FreeDigitalPhotos.net

Data Quality and Data Quality Assurance

This post is on data quality and how to go about assuring high data quality. View my video on Data Quality (I have explained multiple examples in detail in it) or read on...

First, let us understand data quality. Put simply, data are of high quality if they do not suffer from data issues. There are many potential issues with data (see examples below). Now, data are used for a number of organizational functions such as on-going operations, dealing with customers, marketing and analysis and decision making. If the data are not of high quality, there are a number of problems. Users get incorrect reports. Time and money is wasted in miscommunication. Bad data can lead to poor decisions. It can frustrate employees and most importantly, it can frustrate customers.

Although data quality assurance is particularly useful for production databases, it can very well be used in software testing as software testers need to ensure high data quality in gold test databases. Now, let us see examples of data issues that bring down data quality. 

Automate tasks in software testing

When the term automation is mentioned, it is common for people to think of automated tests. But, time and effort can also be saved by automating other tasks such as generating test data and reporting test results automatically. A few factors should be considered for effectively deciding whether to automate a task or not. Last year, we had fruitful discussions on this topic within the STS group.

Let us see some candidate tasks for automation. Testers spend a lot of time on these tasks. If such tasks are fully or partially automated, the saved time can be spent on testing the system.

1. Test identification based on risk or other factors

List all the available test cases along with related features, components and priorities. The automation can take the inputs e.g. impacted features and generate the list of test cases that should be executed on the new application build.

2. Test estimation

Automation can be used to help estimate the test effort and duration. This can be done based on the preferred estimation approach. Whether by querying the historical data for actual efforts and durations, or by applying a custom formula to estimate test effort and test duration.

3. Generation of test data

Build a library of business rules for your test data. Build the initial seed data manually. The automation can use the seed data and generate test data based on the chosen business rules. Since test data generation is usually consumes much time, the automated generation can be performed ahead of time. Then, the pre-generated test data can be used directly during test execution.

4. Generation of bug reports

This automation can be built into the test automation framework. Whenever an automated test script confirms an error, it logs into the bug tracking system and reports a bug with required information. Such as bug title, steps to reproduce, test data used, environment used and so on.

5. Generation of test reports

This automation can execute queries against the test management system (and bug tracking system, if different) and generate test reports. Even distribute them by email or publishing to a website.

6. Release notes preparation

Release notes contain both static and dynamic data. This automation can execute queries on the test management system to retrieve the dynamic data such as features passed, bug fixes passed and known bugs.

Before prioritizing the automation development, always analyze the following factors.

a. Degree of automation achievable

b. Skills required to automate

c. Effort required to automate

d. Number of proposed users

e. Effort required to train users

f. Effort required to execute automation

g. Effort required to maintain automation

h. [Important] Manual effort saved

In the future, I see a number of such tasks automated with the help of vendor tools or bespoke in-house automation.

Let me know if you liked this post. I would love to know your thoughts on this topic.

Image Courtesy: Ambro / FreeDigitalPhotos.net

Tips to build your test data

If you want to know what is test data, view my video on Test Data. Now, let us see the different types of test data:

Test data types

a. Application configuration data
Your application likely needs test data to function (or even launch). Examples of application configuration data include the information to connect to database(s), admin user name and password and server information (e.g. email server) in order to send email notifications.

b. Application data
Other than the above, an application may require application specific data. Examples of application specific data are the menus/ links (and their hierarchies) and detail item information (e.g. item name, item details and item price).

c. Customer or user data
The users creating your application may create their own data within the application. Examples of customer or user data are user details (name, address and other personal details), user searches and user transactions (items browsed and purchased).

Tips to build test data

The questions that you should consider when creating or sourcing the test data for your tests are:

1. Does your application handle blank test data?
In case, your application does not find a particular test data, it should not crash. It should either display an informative (just the required information and no more; otherwise it may be security risk) message or it should display no data but working functionality. For example, if the information to connect to a database is missing, the application should throw an appropriate error message. If the user has not entered any required details, it should prompt the user to enter those details first.

2. Does your application handle invalid test data?
Data input to an application may be invalid in terms of data type (e.g. character data provided instead of the required numeric data), data size (insufficient or excessive size), outside the valid data range or just incorrectly formatted (dots instead of spaces). There are several approaches to deal with invalid test data. An application may reject the invalid test data, provide valid options for the user to select or attempt a conversion to valid data. In any case, invalid data should not be accepted by your application.
Even if the application handles new invalid data correctly, you should test the existing data for invalids. See the articles on database testing for details.

3. Does your application handle valid test data across the entire range of such data?
Of course, your application should accept valid data. However, using a single test data value for a range is not sufficient. Your test data should include the boundary values and several values (you should ask yourself how many values are reasonable) in between.

4. Does your application store confidential user data?
If you use a copy of the current production data, you could be exposing confidential user information to unauthorized persons. You could consider using only the application configuration data and application data from production. Another way to prevent exposure of confidential user information is to obfuscate the customer data from production.

5. Does the application handle the data volumes present or likely in production?
If your test data includes only a handful of data values and there are thousands of data values in production, your testing will not be realistic. You should match your test data quantity with that in production. Further, for realistic tests, your test data values should not be simple duplicates but vary as in production.

6. Is new data released into production from time to time?
You should test the data being released into production independently as well as with your application. After testing, you should update your test database with the new test data released to production.

Automatic test data generation

Among other things, you need test data to execute tests. You are required to design the test data to drive your test cases. If the test data is not already listed with your test cases or you run tests in an exploratory manner, you need to design the test data on the fly. Generating your own test data takes away your valuable time. Generating test data by hand is especially time consuming if you have to run automated tests (e.g. functional tests or performance tests) which need a large quantity of distinct test data.

Of course, if the production data is available, you may get its copy and use it for test. However, this is not a very wise approach. Using live data in test may expose confidential customer information to unauthorized people. In fact, acts like HIPAA address the security and privacy of data.

The good thing is that you need not rely on generating test data by hand. Several tools are available that can help you create large quantities of test data based on the constraints or conditions specified by you. Automated test data generation has the following benefits:

Fast	If the number of test data items is large, automated test data generation may be substantially faster than creating the same test data by hand.
Accurate	Since test data generation is a tedious task, you are likely to make mistakes. Test data generated by a tool can be much more accurate.
Can use built-in algorithms	The test data generator may implement algorithms to generate correctly formatted special data like postal codes and email addresses. Therefore, you need not look up the algorithms for generating the special test data yourself.
Create both valid and invalid test data	Just as you can create valid test data, you can also create invalid test data by specifying incorrect constraints. Further, by specifying the quantity of the valid and invalid data, you can control the percentage distribution between valid and invalid data.
Data exportable to multiple formats	The automated test data generators usually provide options to either generate the test data directly in the desired format (e.g. Excel, CSV or SQL) or export to the desired format.
Create a professional look	Since automated test data generators use their own databases to source data, the test data added/ edited in your application looks professional. This way, you can avoid test data that looks odd (e.g. Name as aa or abab12). This is important in a way because the test data used by you is stored in your application and reported in your bug reports. If your client or management looks at your bug reports, you would feel better if the test data looks good (e.g. Name as Jackson or Burke).

Even though an automated test data generation may offer so many benefits, you should remember that it is just another software. Like other software, it may or may not have all the features you need or always generate the correct data. However, if you are willing to try automated test data generation, you could be boosting your productivity or the productivity of your team.

Note: I said "among other things" since sometimes it is necessary to populate an application with test data before the application can even function (fully).