Test Continuously With Service Virtualization

Access the dependent services your application needs to interact with for accurate and thorough testing even if they are unavailable or incomplete at the time of testing.

When you can’t access the relevant service, its associated interdependencies are also a black box. Service virtualization studies the actual behaviors and replicates such interdependencies in the virtual equivalent, delivering accurate responses.

Incomplete or unstable services can lead to failures and delays in testing. A virtual equivalent is more stable, delivering reliable automation and peace of mind while testing.

Testers don’t need to write complicated scripts. Script-free and codeless tooling allow even novice testers to participate. In the occasional case where scripting is a must, Parasoft supports a variety of programming languages.

Parasoft’s service virtualization studies the application’s actual calls to live services to accurately model requests and responses in the virtual version. A service definition can also be applied for better correlation.

It’s difficult to make a dependent service intentionally behave in a bad way but these extremes need to be considered for accurate testing. Parasoft ensures that the virtual service meets a variety of testing conditions for more thorough testing.

Modern software practices involve multiple operations teams working on pieces of the environment separately and then bringing them together through APIs to form the end product. When waiting for access to a piece of the application, DevOps teams can use service virtualization to simulate those missing pieces so they can continue their application development.

Use service virtualization to make sure your application can handle the full capacity that is needed for high-demand response times, such as holiday sales. Often the cost of maintaining a live performance environment is close to equaling a company’s production environment. By using service virtualization, companies can simulate the dependencies of the APIs that they are testing and cut the cost of performance testing substantially.

Companies have vast amounts of data that form a major part of their interactions with their customers, for example, a bank. This information may include PII (personally identifiable information) such as social security numbers. It is not appropriate to allow testers to have access to real production data sources for their purposes. Service virtualization gives testing teams the ability to generate data synthetically, for example, properly formatted and masked social security numbers, which they can then use for testing functionalities.

Companies can use service virtualization to train employees in an application they will use on a day-to-day basis. This precludes trainees from having to train in the actual production version of the software, which may be tied to multiple complicated applications, and prevents them from modifying or deleting any sensitive data.

Businesses often expose APIs to customers or business partners, including test environments to facilitate easy integration. Maintaining these test environments for partners has a cost, just like with any other environment. Service virtualization is an excellent technique for offering partners access to sandbox environments of your APIs to integrate more easily at a lower cost.

No need to create all or even a large part of the actual system behavior that you are virtualizing; you need only implement the essential transactions. And don’t attempt to emulate ad hoc requests. If you run across a bad test case from flawed input, you can generate a virtualized transaction that replicates it.

Only include behaviors in your virtual service after you understand how the cases interact with them. Your test plan should delineate precisely what transactions to include in the virtualization. Allow the test plan to drive what transactions you will be virtualizing.

Be sure you build test scenarios that provide validation of the system you’re testing, not the service virtualization. Simulate an ideal replica of the dependent component. At the same time, the virtualization should be intelligent enough to let the client finalize a workflow. For example, when UI testing for a bank, the virtual service need only cover the correct depiction of a withdrawal or deposit; no need to verify account balance.

Every team has its individual requirements and when they create their own versions of virtual assets, no harm results if some redundancy occurs. Attempting to create service virtualizations that multiple teams must use only generates another limited service. Best practice is for the team that developed the real service to also create the virtual service because they have the local expertise.

Random responses won’t produce a duplicatable functional test. If you need to vary data you receive from a certain request, the best way is to create a non-changing response set for a group of comparable inputs. Instead, vary the traffic dissemination with random inputs.

Matching multiple transaction demands that have the exact same response means high maintenance. For example, match just the last digit of an account number. Mapping multiple unique account numbers to a single transaction addresses the problem of performance test database caching.

Most test plans have fewer positive test coverage scenarios and more negative ones. This helps when developers spot exceptions. You can more easily recognize failure situations in a narrow test case rather than in system-wide situations.

Consumers understand the behaviors on which they rely. In a normal SOA (service-oriented architecture) environment, the service owner is unaware. Thus, a close relationship with the owner of the service is still essential because, as mentioned, local expertise can enable smarter, more scalable services.