What is Test Data: Significance, Applications, and Challenges
Table of Contents
What is test data in software testing?
In simple terms, test data definition is this: Test data is the selected data sets used to find flaws and make sure that software works the way it’s supposed to.
Testers and engineers rely on test data sets, whether assembled manually or with specialized test data generation tools, to verify software functionality, assess performance, and bolster security.
Expanding on this concept, what is test data in testing? Beyond mere data sets, test data includes a range of input values, scenarios, and conditions. These elements are carefully selected to validate whether the deliverables meet the rigorous criteria of quality and functionality expected from software.
To get a better grasp of test data definition, let’s explore various types of test data.
What are the types of test data?
While the primary goal of testing data is to ensure that the software behaves as expected, the factors affecting software performance vary greatly. This variability means that testers must use different types of data to assess the system’s behavior in different conditions.
So, let’s answer this question—what is test data in software testing?—with examples.
- Positive test data is used to test the software under normal operating conditions, for instance, to check if a car runs smoothly on a flat road without any obstacles.
- Negative test data is like testing the car’s performance with certain spare parts malfunctioning. It helps identify how the software responds to invalid data inputs or system overload.
- Equivalence class test data helps represent the behavior of a specific group or category within the software to test, in particular, how the software handles different types of users or inputs.
- Random test data is generated without any specific pattern. It helps ensure that the software can handle unexpected scenarios smoothly.
- Rule-based test data is generated according to predefined rules or criteria. In a banking app, it can be transaction data generated to ensure that all transactions meet certain regulatory requirements or that account balances remain within specified limits.
- Boundary test data checks how the software manages values at the extreme ends of acceptable ranges. It’s similar to pushing some piece of equipment to its absolute limits.
- Regression test data is used to check if any recent changes to the software have triggered new defects or issues.
By using these different types of test data, QA specialists can effectively assess if the software operates as intended, pinpoint any weaknesses or bugs, and ultimately enhance the system’s performance.
But where can software teams obtain this data? Let’s discuss that next.
How is test data created?
You have the following three options to create test data for your project:
- Cherry-pick the data from the existing database, masking customer info such as personally identifiable information (PII).
- Manually create realistic test data with rule-based data applications.
- Generate synthetic data.
Many data engineering teams rely on just one of the approaches, too often choosing the most time-consuming and effort-intensive method of test data generation. For example, when picking sample data from existing databases, engineering teams must first extract it from multiple sources, then format, scrub, and mask it, making it fit for development or testing environments.
Another challenge is ensuring that data meets specific testing criteria: accuracy, diversity, specificity to a particular solution, high quality, and compliance with regulations on protecting personal data. However, these challenges are effectively addressed by modern test data management approaches, such as automated test data generation.
The Syntho platform offers a range of capabilities to handle these challenges, including:
- Smart de-identification when a tool automatically identifies all PII, saving experts time and effort.
- Working around sensitive information by replacing PII and other identifiers with synthetic mock data that aligns with business logic and patterns.
- Maintaining referential integrity by consistent data mapping across databases and systems.
We’ll explore these capabilities in more detail. But first, let’s delve into the issues related to creating test data so you are aware of them and know how to address them.
Test data challenges in software testing
Sourcing valid test data is a cornerstone of effective testing. However, engineering teams face quite a few challenges on the way to reliable software.
Scattered data sources
Data, especially enterprise data, resides across a myriad of sources, including legacy mainframes, SAP, relational databases, NoSQL, and diverse cloud environments. This dispersion, coupled with a wide range of formats, complicates production data access for software teams. It also slows down the process of getting the right data for testing and results in invalid test data.
Subsetting for focus
Engineering teams often struggle with segmenting large and diverse test datasets into smaller, targeted subsets. But it’s a must-do since this breakup helps them focus on specific test cases, making it easier to reproduce and fix issues while keeping the volume of test data and associated costs low.
Maximizing test coverage
Engineers are also responsible for making sure that test data is comprehensive enough to thoroughly test defined test cases, minimize defect density, and fortify the reliability of software. However, they face challenges in this effort due to various factors, such as system complexity, limited resources, changes in software, data privacy and security concerns, and scalability issues.
Realism in test data
The quest for realism in test data shows how crucial it is to mirror original data values with utmost fidelity. Test data must closely resemble the production environment to avoid false positives or negatives. If this realism isn’t achieved, it can harm software quality and reliability. Given that, specialists need to pay close attention to detail as they prepare test data.
Data refresh and maintenance
Test data must be regularly updated to reflect changes in the production environment and application requirements. However, this task comes with significant challenges, especially in environments where access to data is limited due to regulatory compliance. Coordinating data refresh cycles and ensuring data consistency across testing environments become complex endeavors that demand careful coordination and strict compliance measures.
Challenges with real test data
According to Syntho’s survey on LinkedIn, 50% of companies use production data, and 22% use masked data to test their software. They choose actual data as it seems like an easy decision: copy existing data from the production environment, paste it into the test environment, and use it as needed.
However, using real data for testing presents several challenges, including:
- Masking data to comply with data privacy regulations, avoid data security breaches and adhere to laws prohibiting the use of real data for testing purposes.
- Fitting data into the test environment, which usually differs from the production environment.
- Updating databases regularly enough.
On top of these challenges, companies grapple with three critical issues when choosing real data for testing.
Limited availability
Limited, scarce, or missed data is common when developers consider production data as suitable test data. Accessing high-quality test data, especially for complex systems or scenarios, becomes increasingly difficult. This scarcity of data hampers comprehensive testing and validation processes, making software testing efforts less effective.
Compliance issues
Strict data privacy laws such as CPRA and GDPR require the protection of PII in test environments, imposing rigorous compliance standards on data sanitization. In this context, real names, addresses, telephone numbers, and SSNs found in production data are considered illegal data formats.
Privacy concerns
The compliance challenge is clear: using original personal data as test data is prohibited. To address this issue and ensure that no PII is used to construct test cases, testers must double-check that sensitive data is sanitized or anonymized before using it in testing environments. While critical for data security, this task becomes time-consuming and adds another layer of complexity for testing teams.
Importance of quality test data
Good test data serves as the backbone of the entire QA process. It’s a guarantee that software functions as it should, performs well in different conditions, and stays safe from data breaches and malicious attacks. However, there’s another important benefit.
Are you familiar with shift-left testing? This approach pushes testing toward the early stages in the development lifecycle so it doesn’t slow down the agile process. Shift-left testing cuts the time and costs associated with testing and debugging later in the cycle by catching and fixing issues early on.
For shift-left testing to work well, compliant test data sets are necessary. These help development and QA teams test specific scenarios thoroughly. Automation and streamlining manual processes are key here. You can speed up provisioning and tackle most of the challenges we discussed by using appropriate test data generation tools with synthetic data.
Synthetic data as a solution
A synthetic data-based test data management approach is a relatively new but efficient strategy for maintaining quality while tackling challenges. Companies can rely on synthetic data generation to quickly create high-quality test data.
Definition and characteristics
Synthetic test data is artificially generated data designed to simulate data testing environments for software development. By replacing the PII with mock data without any sensitive information, synthetic data makes test data management faster and easier.
Synthetic test data lowers privacy risks and also lets developers rigorously assess the app’s performance, security, and functionality across a range of potential scenarios without impacting the real system. Now, let’s explore what else synthetic data tools can do.
Address compliance and privacy challenges
Let’s take Syntho’s solution as an example. To tackle compliance and privacy challenges, we employ sophisticated data masking techniques along with state-of-the-art PII scanning technology. Syntho’s AI-powered PII Scanner automatically identifies and flags any columns in user databases containing direct PIIs. This reduces manual work and ensures accurate detection of sensitive data, lowering the risk of data breaches and non-compliance with privacy regulations.
Once columns with PII are identified, Syntho’s platform offers mock data as the best de-identification method in this case. This feature protects sensitive original PII by replacing it with representative mock data that still maintains referential integrity for testing purposes across databases and systems. This is achieved through consistent mapping functionality, which ensures that the substituted data matches the business logic and patterns while complying with regulations like GDPR and HIPAA.
Provide versatility in testing
Versatile testing data can help companies overcome the challenge of limited data availability and maximize test coverage. The Syntho platform supports versatility with its rule-based synthetic data generation.
This concept involves creating test data by following predefined rules and constraints to mimic real-world data or simulate specific scenarios. Rule-based synthetic data generation offers versatility in testing through various strategies:
- Generating data from scratch: Rule-based synthetic data makes it possible to generate data when limited or no real data is available. This equips testers and developers with the necessary data.
- Enriching data: It enriches data by adding more rows and columns, making it easier to create larger datasets.
- Flexibility and customization: With the rule-based approach, we can stay flexible and adapt to different data formats and structures, generating synthetic data tailored to specific needs and scenarios.
- Data cleansing: This involves following predefined rules when generating data to correct inconsistencies, fill in missing values, and remove corrupted test data. It ensures data quality and integrity, particularly important when the original dataset contains inaccuracies that could affect testing results.
When choosing the right data generation tools, it’s essential to consider certain factors to make sure they actually ease the workload for your teams.
Considerations when choosing synthetic data tools
The choice of synthetic data tools depends on your business needs, integration capabilities, and data privacy requirements. While every organization is unique, we have outlined the key criteria for selecting synthetic data generation tools.
Data realism
Ensure that the tool you consider generates test data closely resembling real-world data. Only then will it effectively simulate various test scenarios and detect potential issues. The tool should also offer customization options to mimic different data distributions, patterns, and anomalies in production environments.
Data diversity
Look for tools that can generate sample data covering a wide range of use cases, including different data types, formats, and structures relevant to the software under test. This diversity helps validate whether the system is robust and ensures comprehensive test coverage.
Scalability and performance
Check how well the tool can generate large volumes of synthetic data, especially for testing complex or high-volume systems. You want a tool that can scale up to meet the data requirements of enterprise-scale applications without compromising performance or reliability.
Data privacy and security
Prioritize tools with built-in features to safeguard sensitive or confidential information when generating data. Look for features like data anonymization and compliance with data protection regulations to minimize privacy risks and comply with the law.
Integration and compatibility
Choose software that seamlessly fits your existing testing setup to facilitate easy adoption and integration into the software development workflow. A tool that is compatible with various data storage systems, databases, and testing platforms will be more versatile and easier to use.
For example, Syntho supports 20+ database connectors and 5+ filesystem connectors, including popular options like Microsoft SQL Server, Amazon S3, and Oracle, ensuring data safety and easy data generation.
Customization and flexibility
Seek tools that offer flexible customization options to tailor synthetic data generation to specific testing requirements and scenarios. Customizable parameters, such as data generation rules, relationships, and constraints, let you fine-tune the generated data to match the testing criteria and objectives.
To sum up
The meaning of test data in software development cannot be overstated—it’s what helps us identify and correct flaws in software functionality. But managing test data isn’t just a matter of convenience; it’s crucial for complying with regulations and privacy rules. Doing it right can ease the workload for your development teams, saving money and getting products to market faster.
That’s where synthetic data comes in handy. It provides realistic and versatile data without too much time-intensive work, keeping companies compliant and secure. With synthetic data generation tools, managing test data becomes faster and more efficient.
The best part is that quality synthetic test data is within reach for every company, no matter its purposes. All you need to do is find a reliable provider of synthetic data generation tools. Contact Syntho today and book a free demo to see how synthetic data can benefit your software testing.
About the authors
Chief Product Officer & Co-founder
Marijn has an academic background in computing science, industrial engineering, and finance, and has since then excelled in roles across software product development, data analytics, and cyber security. Marijn is now acting as founder and Chief Product Officer (CPO) at Syntho, driving innovation and strategic vision at the forefront of technology.
Save your synthetic data guide now!
- What is synthetic data?
- Why do organizations use it?
- Value adding synthetic data client cases
- How to start
