What Is Service Virtualization: Examples & UseCases

What is Virtualization?

Virtualization is the process of creating virtual representations or versions of network resources, storage devices, operating systems, computer hardware, and other physical resources.

It facilitates running more than one virtual environment on a single system. As a result, it maximizes the utilization of resources, enhances efficiency, and enables higher flexibility while managing the IT infrastructure.

Types of Virtualization

Virtualization is an umbrella term for various subcategories, each serving a different purpose. Here, we’re going to take a look at three different types of virtualization. They are server virtualization, desktop virtualization, and storage virtualization. Let's dive into the details.

• Server virtualization: Server virtualization consists of multiple virtual machines as a part of a physical server. Each VM runs its own applications and operating system, enabling improved resource utilization, reduced hardware costs, and server consolidation.
• Desktop virtualization: In desktop virtualization, multiple virtual desktops run on one server or physical machine. Users have remote access to these desktops from their devices, offering flexibility in desktop environments and centralized management.
• Storage virtualization: Storage virtualization facilitates abstracting storage area networks, hard drives, or other physical storage devices into logical units. Some of its striking benefits include efficient storage, resource allocation, snapshots, data application, and centralized management.
• Network virtualization: Network virtualization decouples network services from their respective physical network infrastructure. It enables the creation of virtual networks, facilitates network, resource management, segmentation, and isolation, and increases overall flexibility.

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What is Service Virtualization?

Service virtualization, or SV, is one of the most prominent software testing and development techniques simulating dependent services or system behavior that might be unavailable or not fully functional during testing and development. Both developers and testers can easily make virtual representations of such dependencies and mimic their behavior and functionality. These representations are also referred to as virtual services.

In case of complexities and software applications, dependencies on third-party systems, APIs, databases, and auto external services are unavoidable. Although they may not be ready to test or accessible when the development is ongoing, service virtualization offers a way to emulate their behavior. It helps in overcoming this obstacle to a great extent.

Why is Service Virtualization Important?

Testers and developers can simulate behaviors and responses of actual services once they decide to create virtual ones. It also helps facilitate interaction with these virtual services in the same way as with real services.

This goes on despite constraints or unavailability of real services. You can customize virtual services for simulating different scenarios, including data variations, error conditions, and various response times, thus enabling thorough software testing across various conditions.

• Testing and development: Since SV offers a virtual representation of dependent services or systems, teams can parallelly develop and test their software applications without being utterly dependent on real systems. This approach assists in recognizing and addressing problems in the earlier phases of the development cycle. As a result, it reduces integration and testing-related costs and time.
• Continuous Integration and Continuous Delivery: CI/CD is a crucial part of the current-day software development scenario. Since teams can easily create virtual versions of any dependent systems or services using SV, it has become increasingly straightforward to build software, test it, and deploy applications consistently without beating around the bush.

  In other words, you don't have to wait for actual services to become available. Some perks accompanying this approach include accelerated software updates, delivery, and quicker feedback cycles.

• Optimizing resources and costs: When we talk about the real world, plenty of services have hefty usage restrictions, limited availability, and are ridden with sky-high costs. SV removes the mandate to access such services during testing and development directly, thus reducing reliance on resources and costs. You can easily share them across different environments and teams, optimizing the utilization of various resources.
• Parallel development and testing: When the level of complication in systems is high, various teams work in amalgamation simultaneously on multiple components relying on interconnected services. Service virtualization facilitates parallel testing and development by offering virtual representations and eliminating dependencies on the stability and availability of actual services. This way, teams can speed up development and continue to work independently.
• Testing for scalability and performance: Teams can easily simulate different scalability and performance scenarios by emulating different behaviors and loads of dependent services. It helps in figuring out potential roadblocks, checking scaling, and determining the ability of an application to handle a high load to optimize the performance of a system.
• Mitigating risks: Several risks associated with using live systems while testing and development are unavoidable. However, service virtualization reduces or somewhat mitigates these risks since you can manipulate virtualized services to simulate exceptional scenarios, edge cases, and error conditions that are tough to deal with in actual systems. You can also gracefully handle failures by recognizing vulnerabilities and enhancing system resilience.

Benefits of Service Virtualization

There are immense benefits to service virtualization in both testing and development environments. For starters, they can isolate dependencies and carry out continuous testing. Let's look at these benefits in detail, along with some others.

• Isolating dependencies: SV helps in isolating applications or systems from difficult-to-configure external dependencies. They simulate their behavior while testing and development activities go on uninterrupted.
• Continuous testing: Continuous testing goes on as SV eliminates bottlenecks as a result of unstable or unavailable dependent services. Parallel testing can go on, enabling teams to recognize and address defects in the early stages of the Software Development Life Cycle to accelerate delivery and time-to-market.
• Reduce time-to-market: Waiting is one of the most monotonous parts of any development life cycle. Unfortunately, sometimes, app developers might have to wait for third-party API releases and internal colleagues.

  When you simulate components through service virtualization, most teams gain the capability to move faster, conduct risk and load testing, and parallel work to foster better productivity.

  Virtualization nudges organizations to bring forth external and internal beta testers right before the API or software application is ready. Most virtualization tools enable developers to switch between a real asset and a virtual environment or risk a lengthy application rebuild. As a result, the time-to-market decreases.

• Increase quality: When developers and testers have a more profound control of their testing environment by testing a software application and evaluating it under a wide array of conditions, the quality of the product automatically increases. Virtualization facilitates all kinds of tests, including functional tests, performance tests, load tests, etc., during development without other functions blocking them.

  Developers can also better understand the interactions of the tests with other system parts when they're capable of replicating the actions of a service within a particular environment.

  Using this as a base, they can design specifically for those requirements as testers simultaneously conduct testing. This approach boosts productivity, mitigates risks, and facilitates early flagging issues.

• Agile approach and continuous integration: Service virtualization aligns with CI methodologies and most Agile practices. That's why it fosters quicker feedback loops and removes wait times due to unavailable services. Therefore, teams can continuously test code changes. It also promotes a seamless collaboration between testing and development teams and boosts software quality.
• Parallel development: Developers can parallel work on various modules of components of an app without unavailable services or dependencies blocking them. This parallel approach to development can significantly decrease time-to-market by enabling independent working of teams and component integrations later down the line.
• Cost reduction: If you want to reduce the cost associated with managing and maintaining physical testing environments, SV is the way to go. Organizations can easily avoid maintaining and provisioning expenses for dedicated software and enforce structure for all systems and services under test. It's easily possible to create, modify, and reuse virtualized services. As a result, You can quickly minimize infrastructure costs.
• Scalability testing: Service virtualization helps conduct scalability testing as it emulates dependent services and tests them at different volumes and loads.

  It also helps identify performance issues and scalability problems in a software product before its deployment into production. Optimizing the scalability of an application in the early phases of the cycle can help organizations cut out expensive performance issues. As a result, users get a smooth experience.

• Better and increased test coverage: VS supports reproducing costly and challenging test scenarios in real environments. For instance, testers can easily simulate unusual responses, performance issues, and error conditions from dependent services. This allows detailed behavior testing under different scenarios, resulting in better and increased test coverage. The end output is a highly robust application.

Limitations of Service Virtualization

Since service virtualization simulates the behavior of dependent components or systems, it offers massive benefits. But despite that, the technology has some limitations you should consider. Some common ones include the following.

• Complexities in real-world scenarios: Despite its accurate simulation of real-world scenarios, service virtualization can come short while creating an exact replica of complex ones.

  For instance, there are some intricate interdependencies in systems and other instances like intricate protocols or extensive volumes of data that might not be a peach to simulate accurately. This is where virtualized services might not be able to capture an entire range of behaviors completely. As a result, discrepancies might arise during the deployment of actual services.

• Considerations of performance characteristics: It's important to realize that virtualized services can't always offer the exact same performance characteristics as the actual services they represent. Developers and testers might uncover low response times from these simulated services. They might also exhibit varying patterns of consuming resources if we compare them to real services. As a result, such discrepancies can affect the analysis and testing of a system’s performance.
• Limited support for specific protocols and technologies: Not every service virtualization tool supports every single technology, data format, or protocol in a specific system. In case there are proprietary technologies or specialized protocols the system depends on, precise virtualization of such components can be challenging.
• Stateful interactions: The simulation of stateful interactions among services is one of the most challenging tasks, mainly if those interactions include maintaining complicated shared resources or session states. An accurate representation of such interactions can pose challenges in a virtualized environment. Therefore, virtualized service behavior might not fully align with the real system.
• Dynamic systems: Systems of evolving and dynamic nature enable service virtualization to keep up with them. When actual services experience frequent updates or changes, it can result in incompatibilities in the virtualized representations. They might also become outdated. Updating and maintaining virtualized services to reflect recent changes can be extremely resource-intensive.
• Cost and effort: It takes a lot of investment and effort to maintain or even implement an SV infrastructure in the first place. Businesses have to take management of the virtualized environment, cost of training and tools, and other similar factors into consideration to overcome this limitation. Sometimes, the effort and cost needed for service virtualization can start outweighing the benefits. Of course, that depends on the complexity and scope of the system.

History of Service Virtualization Concepts

This concept emerged in the software testing and development field to cater to obstacles in testing complicated systems dependent on various interconnected services. Its history is traceable to the earlier phases of the 2000s when it was obvious that the IT sector will need more effective and efficient testing methodologies.

• Early software testing challenges: When software testing was in its earlier phases, testers used to determine the functionality of applications in isolation without taking other services or components into account. As a result, this approach couldn't capture real-world complexities and gave rise to incomplete testing. However, with more interconnectability and distribution of software systems, the requirement for better comprehensiveness in the testing approach emerged.
• Service-Oriented Architecture (SOA): After realizing early software testing challenges came the emergence of a service-oriented architecture. It gained popularity in the early 2000s as an approach to software design. It advocated creating software applications from services that facilitated communication with each other via a network and were loosely coupled. Despite the reusability and flexibility of SOA, it gave rise to testing challenges since services weren't always fully developed or weren’t 100% available during testing.
• Introduction of stubs and mocks: Developers began using mocks and stubs to address testing challenges in a service-oriented architecture. Stubs allowed them to carry out component testing in isolation with their predetermined request responses. On the other hand, even though mocks didn't offer actual implementations, they did help simulate the behavior of various dependencies. All in all, both mocks and stubs were helpful in decoupling components and carrying out independent testing.
• Evolving into service virtualization: With increasing software system complexities, mocking and stubbing were no longer enough to handle testing challenges. SV exhibited better comprehensiveness for simulating dependent service behavior and created virtual services replicating real service behavior. This allowed testers to proceed despite high costs, unreliability, and unavailability of services in different testing environments.
• Commercial tools and standards: In the beginning of 2010s and even late 2000, plenty of commercial frameworks and tools came up with SV. Testers and developers could define these virtual services, configure them, simulate different scenarios, and manage dependencies. In addition, the International Telecommunication Union or ITU offered best practices and guidelines for SV implementation.
• Integration with DevOps and continuous testing: As Continuous Testing and DevOps gained prominence, the relevance of service virtualization also increased. As we already know, DevOps imparts a special focus on quick feedback loops, automation, and collaboration throughout the SDLC. SV facilitates an efficient and reliable software testing environment by removing external service dependencies and enabling continuous testing. At the same time, service virtualization also reduces bottlenecks generated by evolving or unavailable services.
• Advancements and future trends: As tools, technologies, and software testing techniques undergo advancement, it leads to the evolution of SV as well. For instance, Artificial Intelligence and Machine Learning have been making their way into SV tools to generate virtual services based on observed behaviors automatically. Kubernetes, Docker, and other containerization technologies help create portable, scalable, lightweight VS environments.

How Does Service Virtualization Work?

Service virtualization replicates how external services of a software application work. They encompass APIs, third-party systems, databases, or another service an application interacts with during development or testing.

It aims to create a simulated environment mimicking how these external services behave, allowing testers and developers to continue with reliable and comprehensive testing for costly, unavailable, or under-developed services. Here is the typical working of service virtualization:

• Identifying dependencies: Usually, in an app, there are plenty of dependencies among different components. They may include third-party systems, web services, databases, APIs, etc. SV begins with the identification of such dependencies and getting a grasp on their interactions and behavior.
• Recording of behaviors and capturing them: Once you have identified dependencies, recording and capturing these components’ behaviors follows. For instance, if you’re working with a web service rendering specific responses based on various input parameters, an SV tool will capture responses and their corresponding inputs while recording.
• Creating a virtual service: After you’ve captured behaviors, the SV tool creates virtual services with the help of this info. The virtual services also replicate the behavior of actual components and services. During test execution, these VSs respond in a way real services do in a real environment.
• Simulation: When the software interacts with virtualized services in testing and development, they respond like real services. Testers and developers can go on with realistic testing scenarios despite actual services, giving hassles in setup or remaining unavailable to test.
• Configuring application behavior: Virtual services are easy to configure to simulate various responses and scenarios. For instance, creating edge cases, simulating extreme loads, verifying the app's behavior under specific circumstances, setting response times, or introducing errors is a breeze.
• Integrating with testing: Once you integrate a virtual service in the test environment, it allows you to test the application against an assimilated service, unlike testing it against a real external service. The software product also interacts with the VS just like it would have with a real service.
• Continuous testing: With the evolution of an application, developers, and testers can update virtual services according to a behavioral modification of the real external service. This update-based process encourages continuous testing under any circumstances.

Service Virtualization vs Stub and Mock

Simulating behaviors and isolating components is an integral part of software testing. Teams involved use one or a combination of various techniques. Speaking of which, all three of them, mocks, stubs, and SV, are a part of these simulating and isolating techniques. They hold a lot of similarities, but in this section, we will go into the details of how they are different.

Service Virtualization: Service virtualization simulates the behavior of dependent components or services in a distributed system so that testers and developers can mimic their response times, behavior, and functionality. It facilitates testing complex scenarios by offering a consistent and controllable environment regardless of whether actual services are available or not or whether they are fully developed or not.

Some of the primary characteristics of SV are as follows.

• SV offers a full-service simulation, including performance, characteristics, responses, and behavior.
• It's widely used in case of unavailability or accessibility issues of actual services, like in complex or distributed systems.
• Development and testing teams can easily carry out end-to-end testing despite incomplete implementation of dependent services.
• SV tools offer advanced features, including record and playback, performance testing, and dynamic behavior modification.

Stub: Stubs simplify the implementations of dependencies that come into play while testing real services and their competence. Typically, developers create them and offer predefined responses to inputs. Stubs simulate specific kinds of behaviors devoid of dynamic behavior, and their static design is such that it returns predetermined values. Primarily, stubs replace tough-to-incorporate dependencies into various test environments.

Some of the primary characteristics of stubs are as follows.

• Stubs offer specific responses, which is their primary focus.
• They simulate the functionality and behavior of dependent services or components, mainly in unit testing.
• The scope of use lies within the same environment or process as the code that's undergoing testing.
• The design is not suitable to replicate the complete functionality of a real service or component.

Mock: Like stubs, mocks are responsible for replacing dependencies while testing. But they impart high focus to behavior and exhibit a dynamic nature, unlike stubs. The sole purpose of their creation is to verify the system's interaction under test with them. Mocks facilitate the setting of expectations by testers and developers on how systems should interact with the mock. Another responsibility is verifying expected interactions, testing inter-competent dependencies, and facilitating collaborations using a behavior-driven approach.

Some of the primary characteristics of mocks are as follows.

• Mocks mimic real objects or real component behavior and concentrate on verifying expectations and interactions.
• They use mocking frameworks for dynamic creation.
• Testing into component interaction is their typical scope of work.
• Mocks don't work well with complex scenario simulation that involves a wide variety of components.

Now that it's clear what these three entities are and their characteristics, here is a summary of how stubs, mocks, and SV differ. All in all, stubs have a primary role to play in unit testing as they focus on isolation and simulation of the behavior of collaborators or individual dependencies.

While mocking creates objects, mimicking real object behavior while verifying outputs and interactions, stubbing involves predetermined outputs or responses being provided without explicitly verifying interactions. A substitute object offers these outputs and responses in stubbing.

In short, it's a simpler version of mocks. On the other hand, service virtualization represents a broader concept simulating the behavior of external competence or services in controlled environments where actual services are expensive, challenging to replicate, or unavailable.

Types of Service Virtualization

Most DevOps teams often leverage two key types of SV:

• Data service virtualization
• API service virtualization

Data Service Virtualization: Virtualization has multiple users if we deal with a large enterprise. However, a sharp surge in use cases is often equivalent to a substantial cost rise. Usually, only two primary use cases of service visualization hold significance for most teams, and data service visualization is one of them.

We already know that most data accessed in the current scenario stems from various third parties, which can invite dependency issues. This makes database maintenance and provisioning a prominent bottleneck due to the lack of training of database admins in the relatively new cloud-based scenario.

They might not possess the skills to efficiently and effectively conduct data migration. Here is when database virtualization services, aka DVS, facilitate decoupling the database layer. As a result, organizations can pool and allocate on-demand database resources.

DVS also enables you to continuously build and test data against real-world scenarios despite the unavailability of data sources. However, once you identify the source, specify the properties they come with. When you pass your requests to a virtualization tool, a DVS generates context-specific and appropriate responses using this data based on different request parameters. Upon modification, the simulated responses of these data sets automatically update.

Like every other virtualized entity, data virtualization reduces dependency friction capable of dragging you down and slowing your pace. Despite data sources not leaving the premise, you can keep moving forward.

The best part is that it's even possible to migrate to another physical server from one using an uninterrupted virtual database. Expenses usually occur when team members are involved. Follow a strictly traditional approach to managing databases. Thanks to record and replay, development and testing go on unhindered, even during the unavailability of databases.

Here are some ways to address the test data issue using SV.

• You can quickly decrease the requirement for huge test data volumes with the help of virtual versions of dependent components or systems. This way, you can test your application with a relatively manageable test data volume that doesn't cause any overload.
• Test the system while keeping customer and user privacy intact. Instead of revealing actual data, leverage the power of service utilization to conduct testing.
• Test data complexity is the major cause of test data issues. With the help of service utilization, you can make updates in a virtual component or system’s behavior, making it easily maintainable and updateable.
• When you decrease the test data to be managed and stored, you can successfully minimize infrastructure requirements along with the need for higher storage spaces.

API Service Virtualization: With the evolution of software applications, it was bound for these apps to uncover new business opportunities, revenue streams, and, most importantly, new capabilities. Application programming, interfaces, or APIs facilitate that, along with specifying what proper communication and interaction between software components looks like.

An API offers a steady foundation on which a quality application rests. Developers can enable third-party apps and build a software application to use the data from an external organization securely.

One of the most common examples includes Uber or any ride-sharing business that taps into the power of Google Maps API. Thanks to the amalgamation, the ride-sharing organization doesn't have to build a mapping infrastructure on its own.

Hence, they can concentrate on offering customers the core value they're supposed to deliver. In return, Google gets a brand new recurring revenue source. All in all, API virtualization drives particular emphasis on your technology stack API level.

API virtualization tools offer users an API’s exact virtual copy or the one you have been integrating with. Once testing is complete, the API can be expensive or unavailable. Look up a real device as it's a peach to simulate responses or emulate a production environment with API in a virtual sandbox that doesn't cause disruptions.

Another good news is that setting up separate server stacks mimicking the production environment is not mandatory since it's easy to simulate API behavior with the help of API virtualization. That's exactly how testing an API to check its integration with others is simple, even if it's not 100% ready.

API virtualization enables you to perform API testing in the early phases of the testing life cycle and more frequently. You can do this separately from other APIs and dependencies around your organization. As a result, your API will only go untested after it's built and will turn out to be stable, secure, strong, and good to go.

Product quality and development escalate as testers test APIs by leveraging SV. Developers need to have a tight grasp on the full capabilities of APIs during heavy traffic and high-load situations along with delays. Load testing, third-party sandboxing, and performance testing facilitate this process for developers.

On the other hand, testers can also test REST, WADL, JMS, JSON, and other protocols with different message types with the help of SV. It's a boon for end-to-end testing that offers multi-layer validation for multiple endpoints.

One of the most prominent ways developers use SV is to enable testers to get a grip on how different messages and events make their way through complex architectures.

They also enable testers to test for work activity, enormous call volumes, and other extreme cases. Testers also investigate downstream integration in accessibility, downtime, slow-paced response times, and other failures. Other forms of testing developers facilitate using service virtualization include testing for security gaps, testing a concept, and finding the most appropriate API for integration.

What is Microservice in Service Virtualization?

A microservice is a loosely coupled, independent, and small system or application component in service virtualization. Its architecture constitutes an approach to creating software systems in which an application consists of varying small services capable of being independently deployed, developed, and scaled.

It’s a good idea to combine SV with microservices and carry out individual virtualization of all microservices. Therefore, simulating the behavior of chosen services in an extensive system becomes easier for testers and developers. Some other benefits of this approach include:

• Isolation: Developers can individually test and work on different components without the need to access other dependencies or an entire system as long as they virtualize individual microservices.
• Parallel development: You can test and develop microservices in parallel, regardless of whether individuals or different teams are involved. It speeds up the overall process of development.
• Scalability: Thanks to the individual virtualization of microservices, you can scale them down or up based on specific service requirements, offering efficiency and flexibility in allocating resources.
• Isolating faults: Troubleshooting and debugging become more manageable as the ease of identifying and isolating issues or flaws in a service increases.

What Languages Does Service Virtualization Support?

The best SV tools offer ample support for various programming languages to create and customize virtual services.

SV tools generally support multiple programming languages to create and customize virtual services. The specific languages supported can vary depending on the tool you are using. However, some commonly supported languages in SV tools include Java, C#, JavaScript, Python, Groovy, and Ruby.

Of course, specific languages always vary depending on your SV tool. For instance, different tools can offer unique language support and capabilities. While choosing, you should always check features and documentation for scripting capabilities and supported programming languages.

Service Virtualization Tools

Besides helping businesses develop, test, and integrate component-based apps while the SDLC goes on despite the unavailability of key components, service virtualization also has other roles.

For example, SV tools duplicate the behavior of unavailable resources, web services, or mainframes, making them fruitful to test systems that call for services teams can't directly access.

They also facilitate continuously updated, effective, and faster testing by reducing or removing bottlenecks. Thanks to multiple useful stubbing or mocking, test environment simulation, and error-reporting tools, SV tools are the most viable path to capturing and addressing faults in the earlier phases of the development pipeline. DevOps teams don't have to rely on third-party service provider inputs. Instead, they can use such products to conduct significant test cycles and reduce defect management.

To sum up, the most widely in-demand features of a good service virtualization tool include

• Mock or stub management and data generation.
• Generating and managing virtual endpoints.
• Virtual service sharing and deployment.
• Continuous testing using automation.
• Bandwidth constraints.
• Customizable error conditions.
• Fault isolation.
• Pairing of XML request/response.
• Development wizard.
• Root-cause analysis.
• Scalable testing.
• Correlation as well as regression analysis between data and performance.
• Dependency and interaction mapping.
• Authorization tools based on roles and data.
• Automated validation of messages.
• Ongoing support for deployment.
• Recording and replaying traffic, routing, testing, and proxies.
• Cloud and on-premise testing deployment.

Examples of Service Virtualization

Now that we understand what service virtualization is, how it works, and its types, let's check out some examples for a better grasp. Some of the most common examples include the simulation of payment gateways, third-party APIs, and web services. Let's take a look at these and some others in detail.

• Simulation of payment gateways: While dealing with an eCommerce app, it's obvious to emphasize the payment gateway. However, since it's a critical service, it might not always be feasible to connect to an actual payment gateway every single time during testing or development. But you can always use SV to simulate its behavior without relying on real ones all the time.
• Third-party API simulation: If you have ever used mapping services, social media integration, or weather data functionalities, such apps are usually tightly integrated with third-party APIs as far as multiple functionalities are concerned. Although these APIs have certain usage limitations, they also need additional setup. SV facilitates third-party API simulation of their responses.
• Database virtualization: Applications depend on databases for storing and retrieving data. Although directly accessing the production database might be impractical during testing or development. But thanks to service virtualization, developers and testers have a clear virtual database mimicking actual database behavior.
• Web service simulation: Web services enable communication of different distributed system components. However, these services may not always be available, and SV comes into the picture through simulating web services.
• Message queue virtualization: Message queues facilitate asynchronous communication among different application parts. It's challenging to simulate their behavior while dealing with complicated routing logic or large message volumes. But when SV emulates its behavior, developers, and testers can work with processing and handling messages without being dependent on actual queues.

  Creating a virtual representation of these dependent services is the core aim. It also allows testers and developers to overcome dependencies on limitations on various external systems by working in isolation.

Steps Before Choosing Service Virtualization

Knowing what you're trying to achieve is important before hopping on the service virtualization bandwagon. You should also know your present environment, critical dependencies, and the readiness of your organization before taking such a vital step.

• Define clear objectives: You need to define the objectives and goals to actualize once you have implemented SV. Do you wish to enhance the software's quality and increase test coverage? Or does it have to be specific about reducing dependencies and accelerating testing and development? Always evaluate whether SV is meant to cater to your specific requirements by understanding your goals in-depth.
• Assess the present environment: You need to get a grip on your current testing and development environment and have clarity on pain points, problems, and obstacles. As we have already understood, SV is beneficial in certain specific areas. Make sure you have related requirements.

  Also, consider some complex factors. They include testing and development delays because of the unavailability of external systems, challenges in test environment setup, limitations in accessing dependent systems, and some mind-boggling dependencies.

• Recognize critical dependencies: Recognize crucial dependencies that profoundly impact testing and development processes. They encompass mainframes, web services, third-party systems, tough-to-set-up components, and external APIs. Once you understand the nature and scope of such dependencies, determining SV requirements will become much easier.
• Evaluate alternative solutions: At one point, encountering dependency obstacles is inevitable. Exploring alternative options goes a long way. Some of these alternatives include establishing a test environment with real instances, even though it's a dependent system, using simulators, sandboxes, mocks, or stubs. Lay out a detailed comparison of service virtualization with such alternatives and deduce what suits your needs the best.
• Research SV tools: You should have information on appropriate SV tools. Furthermore, you should evaluate their compatibility with the tech stack, simplicity, capabilities, features, vendor support, etc. Take factors like scalability, performance, integration with existing tools, capabilities for data manipulation, protocol support, and so on into consideration.
• Consider whether your organization is ready: It's crucial to evaluate essential factors, such as whether the teams involved are willing to invest resources and time to maintain the solution or even implement it in the first place.

  Other factors include the depth of acceptance and understanding of the concepts of SV and how skilled your testing and development teams are. Key stakeholders should also support SV to make it a success.

• Proof of Concept (PoC): Conduct a POC to validate the feasibility and effectiveness of SV in your context. Choose a use case or a representative project to simulate dependencies and evaluate outcomes. In addition, measure impact and get feedback from testing and development teams.
• Choosing between open-source and commercial products: Choosing between a commercial or an open-source product is a crucial decision. Open-source service virtualization tools exhibit more customizability and modularity and don't have any additional vendor lock-ins or licensing costs.

  It makes them a wonderful choice for businesses prioritizing flexibility and precision. On the other hand, if your organization is looking for multiple features in one package, professional product support, and a lower entry-level, commercial tools are the way to go.

• Analyzing cost and benefits: Conduct a detailed analysis using cost and benefits as the core parameters. This practice will help determine the resource and financial implications of welcoming service virtualization with open arms. Take into account factors such as time-to-market, potential ROI, maintenance, training, infrastructure needs, and licensing costs.

Once you undergo all these steps, it becomes easier to make an informed choice regarding how right service virtualization is for your organization or whether it's a requirement. If it is, choose the tool that works for your specific organizational needs.

Popular Use Cases For Service Virtualization

Service virtualization simulates the characteristics and behavior of dependent services or systems in controlled environments. SV also creates a virtual representation of unstable or unavailable components and facilitates software testing and development teams to curb some major hurdles. Some of the most popular SV use cases include the following. Let's take a look at them in detail.

• Testing in isolation: One of the most prominent use cases of service virtualization is isolation. Testers can easily verify the performance and functionality of software applications with the help of virtual services, mimicking expected outcomes from real services.
• Performance testing: Performance testing is the most common use case when testing and development teams deal with limited availability, legacy systems, or third-party services with usage restrictions. Thanks to the simulation of critical performance characteristics, firms can easily conduct detailed assessments on how well their software applications are equipped to recognize potential issues and take care of varying load conditions.
• Agile development: When SV remote external system dependencies, it offers teams a boost of productivity with virtual services mimicking expected responses. Therefore, developers and testers can continue testing and building apps without waiting around. This facilitates an Agile development scenario and skyrocketing SDLCs.
• Parallel development and integration testing: When different teams collectively work on various components part of an extensive system, SV enables independent testing and development of those components. Virtual services also facilitate integration testing and parallel development without mandatorily requiring the complete availability of a system.
• API development: API developers can easily virtually represent the APIs they are in the process of building. This approach enables other customers or teams to begin development against the virtual APIs, regardless of whether the implementation is finished. This way, teams can get early feedback and ensure maximum compatibility even during ongoing development.
• Continuous Integration/Continuous Delivery (CI/CD) Pipelines: Regarding CI/ CD pipelines, SV plays a crucial role as developers test applications against virtual service representations of one or more dependent services. It also paves the way for automated deployment and testing, reducing the human intervention requirement. All in all, it contributes to enhancing the overall quality of software.
• Cost cutting: When you virtualize dependent services, it cuts maintenance and access costs for external systems. Businesses can also reduce usage, infrastructure, and licensing expenses with the help of virtual services when development and testing teams often need access to actual services.

Of course, this handful of popular use cases constitutes just the tip of the iceberg for service virtualization. When developers and testers use these techniques, they can attain maximum cost-cutting, speed, and flexibility, thanks to decoupling from external system dependencies.

Therefore, teams involved can effectively deliver quality software and keep progressing despite constraints and limitations because of unstable or unavailable components.

The Future of Service Virtualization

If we are futuristic, we can see realistic and dynamic testing environments through microservices architecture or cloud infrastructures.

Of course, it’s just a tiny fragment of the massive potential this technology holds. Service virtualization will continue to evolve with technological advancements and changing trends. Some potential considerations include:

• Increased adoption: In the future, more and more organizations will begin to recognize how beneficial service virtualization is, which would lead to its increased adoption.

  As we have already discovered in the previous sections, SV leads to cost reductions, better software quality, and a quicker development cycle. Software development is just one arena where the technology has shown so much use. Other upcoming sectors like IOT, artificial intelligence, and cloud computing will likely integrate more SV techniques.

• Integration with new and emerging technologies: When new technologies emerge, we will need something to integrate them, and service virtualization plays a crucial role in making it possible. For instance, SV can test and simulate intra-device interactions in IoT, ensuring interoperability and smooth communication. It can extend similar support in integrating and testing AI systems, edge computing, blockchain, and other similar technologies.
• Cloud-based service virtualization: Cloud computing is on its way toward becoming the heart and soul of development and testing, along with plenty of other IT tasks. It won't be a surprise if service virtualization completely starts leveraging cloud-based infrastructure in the hopes of more cost-effective and scalable solutions. It can offer seamless collaboration, on-demand resources, and a straightforward deployment across various distributor environments and teams.
• Improved simulation capabilities: Simulation capabilities are likely to become more sophisticated in the future, including simulating high-fidelity real world scenarios, edge cases, and even complicated system behaviors. With the integration of AI and Machine Learning, simulation techniques will become more advanced, where intelligent virtual services take key insights from real data and exhibit better adaptability to changing scenarios.
• Interoperability and standards: With SV spreading its wings further, we might require specific interoperability standards for maintaining compatibility among various service virtualization platforms and tools. It includes defining common APIs, formats, and protocols that have the power to facilitate seamless collaboration and integration across different SV solutions.
• Testing security and performance: Two of the most critical concerns for both developers and testers are the performance and security of a software product. SV enhances application performance and increases the resilience of systems under different conditions. Therefore, future advancements will likely emphasize improving these two parameters within a virtualized environment. This would enable businesses to take steps towards proactively mitigating risks by identifying hidden vulnerabilities.
• Continuous and real-time testing: Continuous testing and delivery are here to stay. So, with their ever-increasing demand, there will come a time when service virtualization will have to adapt to offer support to real-time testing. It could include real-time simulation of complicated interactions with different external services, monitoring and capturing production data, integration with live systems, etc.

Conclusion

Service virtualization promotes a more cost-effective and efficient approach to cater to an organization's testing needs. Businesses no longer have to rely entirely on costly real test environments.

Neither do they have to manage and provision various service instances constantly. Instead, virtualization promotes scalable and lightweight simulations to simplify the maintenance and setup of the test environment. It also lowers infrastructure costs, making iterations and experimentations easier for teams during the Software Development Life Cycle.

Some dependency challenges are inevitable. With SV, teams become empowered to overcome them while improving efficiency and increasing application quality.

As long as developers and testers are judiciously utilizing SV and aligning it with specific project requirements, there's no reason why organizations can't leverage service virtualization to optimize development and testing.

About author

Veethee Dixit is a Computer Science Engineer by degree and a passionate writer by choice. Credit for her profession as a web content writer goes to her knack for writing combined with a technical background. You can also follow her on Twitter.