The number of software that exists today is numerous. Many software vendors consistently strive to develop innovative software solutions for low costs. This has been made possible by developers that adopt agile development methodologies as well as those that follow DevOps, a term which refers to the combination of tools, cultural philosophies and practices that enhances an organisation’s ability to deliver services and applications at high velocity. Consequently, testing teams are required to process test data promptly. It is in this respect that test data management becomes relevant. This blog will describe the different forms of test data management that can be applied.
What is Test Data?
Test data refers to the inputs given to software during test execution to assess whether the software is functioning. Hence, any type of data affected by software execution while testing is known as test data. Software engineers use test data mostly for two things. First, it could be used to verify that certain inputs provide an expected result (positive testing), or second, it may be used to monitor whether the software can handle unusual inputs. The higher the quality of test data, the higher the possibility of the software being robust. This is because, through testing data, software developers can test multiple situations that could potentially affect the software negatively and rectify the problem before it is officially launched. Hence, test data allows developers to build software that boosts effectiveness, reduces costs, safeguards privacy and can withstand data breaches. Consequently, test data is centric to ensure the software provides a superior end-user experience.
To carry out testing, it is necessary to produce and consume large amounts of data. This will depend on the testing environment and test cases used. A person can generate test data either manually or copy data in bulk from the production to the testing environment or by using legacy client systems. Alternatively, test data can be generated through automated tools such as AI. This entire process of creating, managing, implementing and delivering test data is categorically known as test data management (TDM).
Five Types of Test Data Management
Multiple forms of test data management currently exist. The most suitable test data management may depend on the type of software application. Here are five of them:
White Box Testing
This refers to an approach adopted by testers to inspect and verify the inner workings of a software system. Hence it inserts inputs and examines the outputs considering the inner workings of the code. This may include the software code, infrastructure and integrations with the external system. Today, it is most commonly used in an automated build process of a modern Continous Integration/Continous Delivery (CI/CD) development pipeline. It is also usually associated with Static Application Security Testing (SAST), which offers feedback on bugs and other vulnerabilities based on the source code or binary. There are various forms of white box testing, including penetration testing, static code analysis, integration testing, unit testing and mutation testing.
Black Box Testing
While white testing requires complete knowledge of the system’s inner workings to carry out tests, black box involves no knowledge. As a result, a tester provides input and afterwards observes the output generated by the system under the test. It evaluates all subsystems, such as UI/UX, the application and web server, dependencies, database and integrated systems and is often used to gain feedback on compliance and security. This is ideal for identifying the system’s response to positive and negative test data and detecting defects in reliability and usability. Black box testing is similar to how end-users do not care how the system is coded, although they do expect an appropriate response to their requests. Thus, the central aim of this form of testing is to assess whether the system delivers on its promise.
Grey/Gray Box Testing
Interchangeably known as Gray or Grey box testing, this form of test data management focuses on security. This is a neutral approach to white box and black box testing. Instead of having all or no knowledge of the inner workings of the code, Gray test data has limited knowledge of the component being tested. A good example of this form of testing is penetration, whereby developers can gain test data that is unbiased and non-intrusive. This is because, here, the tester would typically know the application’s internal components, although it would lack knowledge on how they interact. Hence, any conclusions made through these tests allow developers to understand the experiences of potential hackers and users.
Application Security Testing
Also known by its abbreviated term AST, this form of testing refers to ensuring the software is protected against security threats. This is done by assessing the security weaknesses and vulnerabilities of the source code. Traditionally AST was a manual process. Hence, organisations use a combination of application security tools to automate it. This includes Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), Interactive Application Security Testing (IAST), Mobile Application Security Testing (MAST), Software Composition Analysis (SCA), Runtime Application Self Protection (RASP) and more. The important aspect of AST is that developers are able to comprehensively understand security concerns and enforce best practices at the development stage. It can also ensure that any issues are fixed before the software is sent for production.
Masked Data
Also referred to as data masking, this refers to data testing strategies that adopt a fake but realistic version of a company’s data. Typically, this is primarily used to secure sensitive data. The advantage of masked data is that, where an attacker attempts to break through, it would only have access to use useless data. Although the data used is fake, it should be noted that it will resemble the real data’s inherent functional properties. Masked data is essential to addressing issues related to data loss, data exfiltration and other threats. There are multiple types of data masking, including static, deterministic, on-the-fly, and dynamic data masking. Examples of data masks applied include situations where data is encrypted so that the data becomes useless unless the viewer has the decryption key.
Testing Data at the Heart of Building Robust Software Solutions
Reputed software vendors often rely on testing data to ensure their software is protected against security threats and can offer reasonable answers to questions asked by the user. Therefore, the software’s success depends on the quality of testing data. Ambitious vendors that strive to solve complex issues through a software solution may have to rely on testing data more. Where this is properly implemented, developers can address defects and drawbacks within the development stage prior to its release, thus eliminating unnecessary costs and delays in sending the software for production and release.
This article is prepared by Cerexio, a leading technology vendor that offers specialised solutions in the Advanced Manufacturing Technology Sector. The company is headquartered in Singapore and has offices even in Australia. Cerexio consists of a team of experts that have years of experience and holds detailed knowledge on a range of subject matters centric to the latest technologies offered in manufacturing and warehouse operations, as well as in predictive maintenance, digital twin, PLC & instrumentation setup, enterprise integrator, data analytics and total investment system.
