Testing an application is one of the most crucial tasks in the SDLC that unlocks the way to the application’s optimized performance, functionality, and rendered user experience. However, the testing processes are often challenging as these cover voluminous data around the functionalities, user activities, performance, security, and much more. It is often complex for testers to have all the necessary test cases and scenarios covered, as these are extensive and involve vast amounts of data. Managing this data seems nearly impossible without the right test data management strategy.
What is test data?
Test data primarily refers to the data leveraged to test a software application. The values of login details like usernames and passwords, transaction details, and much more account for test data.
Test data are of two major types
- Static data
- Transactional data
Static data includes names, currencies, countries, etc., which are not highly sensitive. On the other hand, transactional data involves data like credit/debit card numbers, information about bank accounts, medical history, identity details, etc.; there is always a risk of the data getting stolen.
Also check: Saving Test Data to Make Debugging Easier
What do we understand by test data management in software testing?
Test data management or TDM in testing is a process that includes the design, planning, storage, and management of a diverse range of test data. The testing team controls the files, data, managing rules and policies throughout the testing cycle. TDM aims to demarcate production and test data and helps maintain essential attributes like test versions and bug-tracking.
Why does TDM matter for businesses?
In several scenarios, the functionalities and data usage work perfectly during staging and testing. However, while releasing the code into production can lead to intermittent errors due to inefficient test data management. It is always quintessential for businesses to deploy an efficient test data management strategy in order to avoid improper data utilization across the organization.
Following are some of the key significance of test data management benefits for businesses
- Eliminating redundancy in test data
Application owners create multiple code copies for development and testing, where most often, the size of these copies is similar to the original code. As a result, the production data has several copies. Due to the voluminous copies, often the owner fails to identify the most useful copies.
An efficient TDM strategy helps owners and managers avoid two fundamental problems, the first being reducing redundancies and the second being seamless management of the copies. By helping to eliminate redundancies, effective TDM helps to reduce storage costs. Additionally, with an efficient TDM suite, app owners and managers can leverage effective algorithms to sort out and identify redundant or duplicate test data and delete unnecessary data, thereby streamlining and managing large volumes of data.
- Driving smart QA analysis
Cognitive QA enables implementing automation seamlessly with quality data. This simplifies deploying robotic process automation and AI and allows to align test functions to business outcomes to better reach the goals. By managing data optimally, TDM helps to efficiently leverage this smart QA approach properly and deliver high-quality end products through smart analysis.
Also see: What is End to End Testing?
- Ensuring the right data quality
TDM enables organizations to meet the test data needs of testing teams by assuring that the test data is of the right quality and quantity with the appropriate format and proper environment. This ensures that the provided data is referentially intact and of the correct size, not too large or small to aptly fit the testing requirement. The data can be provisioned by synthetic data creation, production extraction, or sourcing from lookup tables.
- Lowering costs despite delayed bug detection
Detecting bugs at the early stages of the SDLC is the most preferred as a delay in this could lead to significant costs for fixing the impact. The requirement for emergency fixes increases as unexpected bugs arise in the later stages and consumes a lot of time to duplicate production data. This often leads to high costs for fixing bugs. However, with the right TDM strategy, organizations can avoid the time consumed by duplicating production data as the required data is segregated correctly and can be utilized at any stage of the SDLC. Hence, the costs don't escalate steeply if bug detection is late.
- Enhancing customer trust
With inefficient TDM, test data coverage might not be optimal, leading to several performance glitches in the apps. Crashes, freezes, and other errors can negatively impact customer trust and might eventually lead to a weakening clientele. Unorganized Test data can impact major functionalities and overall performances, thereby affecting customer retention.
Test data management best practices
Exploring test data
Data can be in diverse formats and in multiple systems. Different teams should select the correct data based on the test cases and requirements. However, locating the data manually can be time-consuming. Therefore organizations need to explore the scope of the data, sort the test data based on test cases and accordingly find a robust test management tool to locate the right data.
Organizations need to determine their test data requirements as per the test cases to optimize the effort dedicated to creating the test data.
This approach involves creating realistic test databases, which are small enough to support rapid test runs but much larger to reflect the diverse production data accurately.
Recommended post: How to Improve Software Security
Masking or de-identifying sensitive test data
Organizations must identify sensitive client and employee data prior to transferring data to the testing environment. After identifying and analyzing sensitive data and testing cases, they should choose the appropriate de-identifying technique.
Updating test data
Refreshing test data enables teams to streamline the testing process and maintain a consistent and manageable test environment that improves testing efficiencies.
Automating the comparison of test data result
Organizations should deploy an automated tool for comparing the baseline test data against results to drive businesses to quickly identify problems that might otherwise go undetected.
Key properties that TDM needs to ensure for better business outcomes
Besides quality, effective TDM processes must ensure several other attributes to help businesses prosper with optimal utilization of the data.
- Availability, in order to ensure that teams can leverage the data at the appropriate time and situation required.
- Realism, to ensure that test data mimics real production data perfectly to efficiently verify how the system behaves under real-world scenario
- Data protection to ensure no compromises are made towards customer data; One of the most prevalent for acquiring test data is production cloning. This resolves the issue of realism but exposes sensitive, personally identifiable information. Such data leakage can cost organizations not only their reputation but also lead to severe financial and legal consequences.
TDM in functional testing
Functional testing is often challenged by low coverage, limited access, high dependency, oversized test environment, and many more. Efficient TDM strategies can help address such issues and ensure the optimal functioning of applications.
The key areas that affect TDM in functional testing are:
- Coverage: The primary factor for data provisioning must involve complete exposure to the possible test cases or test scenarios. The data provisioned should ensure covering
- Positive scenarios: these refer to valid values that should make the test case pass
- Negative scenarios: these include invalid values that should return to appropriate error handling
- Boundary conditions: these refer to data values at the extremities of the possible values
- Each of the functional flows defined in the requirement that involves data for each flow
- High re-usage: Test data like account details, client IDs, country codes, etc., can be easily reused across diverse test cases to ensure optimizing the test data pool. Static and basic transaction data for an app can be baselined to be restored or retrieved for maintaining the release testing at regular intervals based on the release frequency.
- Low volumes: Single data sets for every scenario are sufficient for the test data need. For similar test cases, repetitive test data may be futile and can significantly consume unwanted time. Depending on single data sets can significantly reduce the execution time.
Data Requirements Gathering Process
During the test case scripting process, the test data requirements at the test-case level need to be documented and marked if they're reusable or non-reusable. This helps to ensure that the test data is well-defined, concise, and well-documented. An effective compilation of the same type of test data meets the requirement of test data to be provisioned.
TDM is apt for functional testing projects that,
- Spend a significant proportion of testing efforts (around 15 percent) on data preparation or data rework.
- Utilize regression test cases that are run repeatedly across releases
- Indicate that a TDM solution with high data coverage can be deployed in the project
TDM in performance testing
Insufficient production-like data and data volume can restrict teams from producing the data required or performance and load testing. For good performance testing, test data preparation is often affected by challenges like the right amount of data, as mentioned, proper coverage, lengthy preparation time, constricted environment availability, and many more. Following are the areas of performance testing where TDM plays a vital role—
- A high volume of data: Effective performance testing requires extensive data in order to ensure flawless performance of the app. Multiple users leverage a particular app at the same time and have several requirements that need to be fed parallelly into the workload model to run a flow of test executions.
- Quick consumption of test data: During performance testing, as the load or stress is induced by multiple users on the app, the data provisioned for them is rapidly consumed, leading to quick exhaustion of the test data.
- Workload distribution: The recent software testing requirements don't involve the use of similar types of data repeatedly. Developers and testers require smart data to efficiently deploy a workload model that covers multiple data types in the use cases, and with each separate workload model, the complexity of entire data creation increases. Test data management plays an important role here in segregating and managing complex datasets.
TDM in automation testing
Though automation testing has become widely popular, creating test data for this is often challenging. Creating data proactively from the front end consumes large volumes of data during the test runs, limits access to dynamic data, and causes insufficient availability of the required environment. A well-defined TDM strategy enables organizations to execute multiple iterations of dynamic data in short intervals by synthetically extracting or creating data using TDM tools.
Key areas in automation testing where TDM assists largely include
- Automation of test data creation: The usual processes of creating data from the user interface front-end or creating or editing data operations in the database are generally time-consuming and might require automation teams to have application as well as domain-specific knowledge. Selecting the right approach for TDM in automation testing is crucial. The correct tools that can efficiently handle sensitive data via masking or synthesis that supports agile principles can be a good fit for TDM.
- High coverage: automation testing requires test data for each of the automated test scenarios. The data requirement may be limited to the regression test pack and still covers a large spectrum of data.
- Rapid data consumption: Like in other forms of testing, in automation testing too, the test data is consumed rapidly, thereby making it essential to deploy a strategy that can align with quick data creation and a relatively shorter life cycle.
Implementing TDM in the testing process significantly boosts the entire process and improves the test outcomes to help ensure the optimal quality of the apps. However, organizations must carefully obtain and understand the nitty-gritty of their test data and align the right TDM strategy to the data for enhanced utilization of the data required for software testing. Implementing the correct TDM requires efficient planning to increase the robustness of the TDM and thereby drive business outcomes and desirable ROI.
Q1. What are the different tips to consider for test data management?
Ans: The major test data management factors that help businesses maximize ROI from testing are:
- Analyze before finalizing the data
- Mimic the production scenario
- Foster automation
- Protect confidential and sensitive data
- Maintain a centralized test data repository
Q2. What is shift-left testing?
Ans: Shift-left testing refers to an approach to software testing where the testing is performed earlier in the software process. This requires the availability of realistic test data early on. Shift-left testing is quite common in agile development, where software development is split into sprints, and each sprint needs its own testing cycles. This makes creating realistic data more complex, often canceling the benefits of agile productivity.
Q3. What are the dependencies in TDM?
Ans: The key dependencies of the test data involve referential integrity, cross-system integrations, or application-specific requirements.
Q4. What type of testing is the test data used for?
Ans: Automation testing requires highly stable, predictable data sets that can easily adapt to higher degrees of variability. On the other hand, performance tests need data to be either production scale or representative of production distributions.