Uncover the idea behind Test Data and find out the best ways to organize test data for successful testing:
In the recent technological transformation era, continuously testers make use of test data in the software testing cycle. They not only gather and retain data from available resources, but also produce vast amounts of test data to guarantee the product’s quality for real-world utilization.
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As testing professionals, we need to endlessly analyze, obtain knowledge, and implement proficient techniques for gathering data, creating, maintaining, automating, and all-inclusive management of data for the purpose of functional and non-functional testing.
This guide will offer you tips about organizing test data to ensure no significant test cases are overlooked because of inadequate data and incomplete setup of test environment.
Table of Contents:
- What is Test Data and Why It’s Important
- Attributes of Good Test Data
The Importance and Meaning of Test Data
As per a survey by IBM in 2016, the processes of locating, managing, maintaining, and creating test data take up about 30%-60% of the testing professionals’ time. This clearly demonstrates that data preparation is a significant aspect and consumes considerable time in software testing.
Figure 1: The Average Time spent on Test Data Management
In other industries, data scientists devote 50%-80% of their time to data organization for modeling. Looking at the importance of data privacy and Personally Identifiable Information (PII), testing professionals have a critical part to play in the testing process.
To business owners, the credibility and reliability of the test data are of utmost importance. Ghost duplicates of test data are perceived as a significant hindrance that can undermine the trustworthiness of any application and its quality control in meeting client requirements.
Test data is integral to the verification and validation of the application under test. Accuracy and completeness are key to identifying and locating defects effectively. Test data can take different shapes, such as system test data, SQL test data, performance test data, and XML test data.
While creating test cases, it’s crucial to have input data that can include various testing scenarios. It’s essential for testers to input the necessary data while test case execution or the application can obtain the necessary input data from predefined data sources.
Preparation of appropriate test data is a critical part of test setup. Testing professionals often refer to it as testbed preparation, where all software and hardware requirements are set with predetermined data values. Testers should not only rely on data created by other testers or the default production data, instead, they should create fresh and relevant data sets based on particular testing needs.
Creating correct and complete test data is important to ensure maximum test coverage. In some instances, it might not be feasible to create completely new sets of data, standard production data could be utilized as long as additional data sets are added. Testers should aim to develop a well-rounded data set over time by using existing sample data or testbeds and adding new test case data for every testing cycle.
Issues in Sourcing Test Data
One of the challenges in creating test data is sourcing the required subset of data. For example, when there are millions of customers and only a few thousand are needed for testing, picking a beforehand sample that’s consistent and statistically viable is important. Identifying and locating the correct subset of data for testing is an advantageous method for testing use cases.
Moreover, there may be environmental constraints and privacy policies to consider. Tools for Test Data Management are built to address these issues; they suggest guidelines based on the standards or catalogs they have. However, caution needs to be taken as it may not always be a safe process and an audit is necessary.
To address the issues in providing test data, it’s important to consider aspects like when and where to carry out test data management, what should be automated, how much investment should be set aside for the development of human resources and utilizing new TDM tools, and whether functional or non-functional testing should be given priority.
Some typical issues in providing test data are:
- Insufficient knowledge and capabilities in using test data generator tools
- Incomplete coverage of test data
- Lack of specificity in data requirements and volume specifications during the collection phase
- Lack of access to data sources for testing teams
- Delay in providing production data access to testers
- Production data’s unsuitability for testing based on business scenarios
- Limited times for generating large data volumes
- Dependencies and combinations of data for testing specific business scenarios
- Dealing with multiple applications and versions of data
- Spending extra time communicating with designers, database administrators, and business analysts for data gathering
- Generating or organizing data during test execution
- Continuous release cycles across multiple applications
- Compliance with legislation on personal identification information (PII)
Developers are tasked with preparing production data for white box testing to enhance the coverage of the application under test. The challenge lies in including all possible testing scenarios and combinations to ensure all-inclusive test coverage.
Considering these challenges, it is crucial to explore different techniques to handle test data design and management to drive effectual testing efforts and increase cost-effectiveness.
Techniques for Preparing Test Data
In order to enhance testing efforts and augment cost efficiency, testers constantly explore different strategies for preparing test data. The outcome is considerably amplified when utilities are combined into the production and testing settings.
Test data has a vital role to play in maintaining the quality of software, and the completeness and coverage of testing are largely dependent on the quality of data. The techniques for prepared test data should concentrate on producing accurate data that covers all the requirement types in each project.
Figure 2: Methodologies for Test Data Management (TDM)
To minimize data preparation efforts and maximize utilization of resources, it’s practical to create a subset of the required data from the production setting. This method reduces time and brings down costs by utilizing existing resources.
Identifying or creating correct data at the start of the test project is critical. The following techniques could be applied: