deployment-tools

The Top Deployment Strategies Explained

deployment-tools

As a DevOps engineer, you need to be familiar with various software deployment strategies and know when to use which one. In this article, we’ll look at what software deployment strategies are available, how they work, and the typical strengths & weaknesses of each.

In software development, a deployment strategy is a set of instructions that dictate how our software code or applications should be transferred from one environment to another during the software development life cycle.

What is a Release

The process of "shipping" new features or bug fixes, usually more than one, to users is known as a software release. A software release can be a patched version, a major new version, or a hotfix for an issue found in a previous version. Software releases go through several development stages before they are ready to be made available to users (in what is called production).

A typical software development life cycle includes the following stages:

  • Development
  • System, Integration, and User Acceptance Testing
  • Staging
  • Production

Your deployment process defines the rules and steps of how software code should be moved (or deployed) from one stage to the next. It is important to have a well-defined deployment strategy because it will help ensure that code changes do not break the software in production and that users always have access to the latest version of the software.

To complete this important job, the DevOps team incorporates deployment procedures into their day-to-day operations. Various approaches have been developed throughout time to help software companies with application deployments.

Rolling Deployment

What Is a Deployment Strategy?

A deployment strategy is a technique used by DevOps teams to launch a new version of their software. These strategies cover how traffic is transitioned from the old version to the new version and can influence downtime and operational cost. Depending on the company's specialty, the right deployment strategy can make all the difference.

Various Types of Deployment Strategies

There are several types of deployment strategies, each with its advantages and disadvantages. The right strategy for your company will depend on your needs and goals.

1. Blue/Green Deployment

This type of deployment process involves maintaining two identical production environments—one is the “live” environment that serves customers, while the other is the “staging” environment. When it’s time to release a new version of the software, the staging environment is switched to live, and vice versa.

Benefit:

  • This strategy minimizes downtime because there is always a production environment available.

Disadvantage:

  • However, it can be costly to maintain two identical production environments.

2. Canary Release

In this strategy, the new version of the software is first released to a small subset of users. If there are no major issues, the new version is then gradually rolled out to a larger subset of users until it is finally made available to the entire user base.

For example, the older version may retain 90% of all traffic for the software at a certain point in time during the deployment process, while the newer version hosts 10% of all traffic. This method helps DevOps engineers to test the new version's stability. It utilizes real traffic from a fraction of end-users at different phases throughout production.

Benefit:

  • Better performance monitoring is possible with Canary deployment. It also aids in the quicker and more successful rollback of software if a new version fails.

Disadvantage:

  • However, it does require more effort and typically, a long deployment cycle.

3. A/B Testing

May, also be called Incremental Rollout

In the A/B testing deployment process, developers deploy the new version alongside the older version. This type of testing is used to compare two versions of a software feature to see which performs better. Version A is the control and is made available to the entire user base, while version B is the test and is only made available to a subset of users.

A/B testing has several deployment process benefits:

  • It allows software developers to compare two versions of a software feature to see which performs better.
  • It is easier and less risky to test a new version of the software on a small subset of users before rolling it out to the entire user base.
  • Developers can easily accept/reject either version.

Disadvantage:

  • Increased user/customer coordination.

4. Feature Toggles (Feature Flags)

Feature flags are a type of deployment strategy that allows developers to turn on or off certain features of the software for different users. This allows developers to test new features without making them available to the entire user base. Feature flags can be used in conjunction with other deployment strategies, such as A/B testing, to help developers test new features before

5. Recreate Deployment

In this deployment approach, the development team completely shuts down the old software, then deploys and reboots the new version. This method causes a system outage between shutting down the old program and booting up the new one.

Benefits:

  • It is less expensive and is primarily utilized when the software company wishes to rewrite the application from the ground up. There's no need for a load balancer since there are no changes in traffic flow in the live production environment.

Disadvantages:

  • This method has a significant impact on end-users since it is unavailable/suspended. Users must wait until the software is reactivated before using it. As a result, few developers employ this technique unless they have no other option.

6. Trunk-Based Deployment

In this strategy, all code changes are first merged into a main trunk or branch. Developers then create a new branch for each new feature. Once the feature is complete, it is merged back into the main trunk. This strategy eliminates the need for long-running feature branches and makes it easier to deploy new changes.

Note: This is more a pre-deployment method of Software Version Control.

7. Ramped Deployment

The ramped deployment method moves from one version to the next in a gradual process. Unlike canary deployment, which replaces instances of the old application version with those from the new application version one at a time, the ramped deployment approach makes its change by replacing instances of the old application version with new applications. The rolling upgrade deployment strategy is another name for this method.

The second method, as the name implies, is to delete the old version from production. When all of its instances are deleted, the older edition is manually shut down. The new edition then controls all production traffic.

Advantages:

  • No need to take the entire application offline for an upgrade.
  • The process is gradual, so it's less risky.

Disadvantages:

  • Takes longer to complete than other methods.
  • Requires more instances to be available during the process.
  • Rollback is more complicated & long.

8. Rolling deployment

For those using containers.

Rolling deployment is a gradual process of replacing pods running the old version of the application with the new version, one by one, without downtime to the cluster. It is less risky and takes longer to complete than other types of deployment, but it doesn't require taking the entire application offline.

Advantage:

  • Lower Risk
  • High Availability

Disadvantage:

  • Only really applicable for container-based architectures.

9. Shadow Deployment

Developers deploy the new version alongside the existing one in this deployment method. Users, on the other hand, won't be able to access it right away. The newest version hides in the shadows, just as its name implies. Developers send a fork or copy of the previous version's requests to the shadow version so they can examine how the new variant will work and if it can process the same amount of requests.

When the shadow version can handle the same load as the original, the traffic is finally routed to the new version, and it becomes live. The cutover from the original to the new version happens without any significant downtime since there's no need to take down or restart either version.

Advantages:

  • valuable feedback can be gathered about how the new version will work in production
  • there's no need to take down or restart either version during the cutover process

Disadvantages:

  • more complicated to set up and maintain than other deployment strategies
  • if not done correctly, it can cause issues with the live version

When to use:

  • when you want to gather feedback about how the new version will work in production
  • when you want to avoid any significant downtime during the cutover process

Deploy Better with a Software Deployment Tool

Managing your deployments without tools can be fraught with danger.

As seen above, the different deployment processes can be quite fragile/awkward, and if done incorrectly could lead to production issues, outages, and the need to roll back.

Using tools to control your "implementation day events" can uplift visibility, improve collaboration, support rehearsal, standardize your operations and also streamline the tasks*.

*Tasks that may be manual or preferably automated.

Fortunately, there are various Release Management tools that can help your organization with the various aspects of Environments, Release Management & Application Deployment.

The best software deployment tools included features like:

  • Release Management Governance for Scale Delivery*

*for managing the End to End Release / Release Train.

  • Implementation Plans (for Deployment Planning)
  • Operational Runsheets / Standardized Operating Procedures
  • DevOps Automation e.g. Software Deployments
  • Orchestrations / Integration with other tools*

*deployment tools, ticketing tools, CI/CD i.e. continuous integration, and continuous delivery tools

  • Deployment Version Tracking

*tracking code deployments across Environment Instances, Components & Microservices.

  • Environment Drift Reports

*supporting holistic, cross-environment, version control

Conclusion

You may use any of these methods to upgrade your applications. Each of these approaches has advantages and disadvantages, and each is appropriate in certain circumstances. The only question now is which one makes the most sense for your DevOps team to utilize.

Consider the demands of your team, project, and company as well as corporate objectives. Also, keep track of how much downtime your business can tolerate and any other cost limitations.

Make your go-live events into non-events!

Uplift your Implementation Planning, and Deployment Management capability today. Find the best software deployment tool (or tools) to help with your automatic deployments.

Author: Mark Dwight James

This post was written by Mark Dwight James. Mark is a Data Scientist specializing in Software Engineering. His passions are sharing ideas around software development and how companies can value stream through data best practices.

Test Environment Management 101

Test Environments Management 101

Test Environment Management 101

Test environments are critical in the software development and software testing process as they allow for quality assurance testing to take place in a controlled setting. Test environments can take many forms, from simulating customer data on a test server to running performance tests on a staging environment. The key is to ensure that your test environment accurately reflects your production environment as closely as possible.

There are many ways to run tests, and most involve testing environments. This post explores test environments from the ground up. Not only will you learn what a test environment is, but who is responsible and what practices are needed.

This post will explore test environments in-depth, discussing everything from what they are to how to set them up and manage them effectively.

Test Environment Management 101

What is a Test Environment?

A test environment is any space in which software undergoes a series of experimental uses. In other words, it’s a place where software testing will you test your code to make sure it works as you intended.

A Test Environment is a type of IT environment that is used for the sole purpose of testing. This could include anything from functional testing to load testing and performance testing.

The main purpose of having a Test Environment is to create an isolated environment, including Test Data, in which development and tests can be carried out without affecting the live production environment.

Test environments are typically made of one or more of your applications, or systems. This includes the physical or virtual hardware, whether on-premise or in the cloud, and the operating system on which such versions of the application software will reside for the duration of prescribed test executions.

Let’s take a look at a few test environment types and gain a deeper understanding of them.

Types of environments

There are typically seven types of environments along any software’s development lifecycle:

  • Development
  • System Testing
  • Integration Testing
  • User Acceptance Testing
  • Performance Testing
  • Staging
  • Production

Each environment has a different purpose, and as such, each one runs the application in a slightly different way.

What is a “Development” Environment?

The development environment, on the far left of the lifecycle, is where the main (latest) branch of a software application is located. This is where developers spend time writing code to create a minimum viable product (MVP) from an initial concept. These environments may be shared within the team, or deployed on people on development instances, say inside a VM or Container on their laptop.

The development environment plays a crucial role in the software development process as it is here that new features or updates are first worked on. Note: It is not unusual to have these testing environments installed on one's laptop.

What is a “System” Test Environment?

Supporting System or Component Testing, a system test environment is a non-production environment, or test bed, that is used to test the specific, standalone, functionality of a system before it is deployed to later test phases. This type of environment is typically configured to resemble the production environment as closely as possible, however, it will probably use stubs (mocks or virtual services) to mimic the behavior of up or downstream systems.

What is a “System Integration” Test Environment

The objective of System Integration Testing (SIT) is to ensure that all software applications and microservices work together as intended and that data integrity is preserved between them.

System Integration Test Environments are used to test the end-to-end integration, with a specific focus on the connection, or interface, points, and the movement of data between the systems. As such System Integration (SIT) testing environments are a combination of several systems that mimic how production systems collaborate.

What is a “UAT” Test Environment?

User Acceptance Testing (UAT) is a type of testing that is used to determine whether a software application meets the needs of the end-user. This type of testing is usually carried out by the end-user, or someone who represents the end-user, such as a business analyst.

UAT testing environments are an end-to-end representation of your Production Environment. It would normally contain one system instance for each production instance. For example, you would have a CRM UAT to represent CRM Production.

What is a "Performance Testing" Environment?

A performance testing environment is a non-production environment that is used to conduct performance tests, that is test the performance of software, typically under load. Performance tests are important to ensure that the software will be able to handle the expected number of users or transactions when it goes live.

Several different factors need to be considered when setting up a performance testing environment or test bed, including hardware requirements, software configurations, and network settings. It is important to have a clear understanding of what needs to be tested and how the results will be used before starting to create the performance testing environment.

What is a “Staging” Environment?

Following on from standard Test Environments, we have the Staging environments. A staging environment is meant to simulate production as much as possible, as such Staging Environments are usually well controlled, near-production level in size and layout complexity.

Simply put, this final non-production environment is used to provide further confidence in the software before it reaches the end destination of production. Note: A Staging Environment may also be used for supporting endeavors like Production Support.

What is a “Production” Environment?

Production Environments is the final stop for any software application. It is here that the application will be used by actual end-users or customers and here we find the production data. Given that it is supporting end users it is common to have the highest spec infrastructure deployed here, that is the highest performing resources like CPU, Memory, and Disk.

In addition, and due to the need for availability, it is common to have important systems configured in highly available and load-balanced layouts. And in conjunction, it is important to have well-defined processes and procedures in place for managing and maintaining them. These processes should cover everything from provisioning, and rollback through to incident management.

It is also important to have monitoring in place so that any issues can be identified and rectified as quickly as possible. This monitored data can also be used to help improve the application over time.

With the above in mind, who sets up these environments & how? Ultimately the Non-Production / Test Environments are managed by a Test Environment Manager.

What is a Test Environment Manager?

Test Environment Manager is a job title that refers to the person responsible for managing and maintaining Test Environments. The TEM is responsible for ensuring that the Test Environments are properly configured, maintained, and meet the needs of the IT project.

The Test Environment Manager is responsible for the day-to-day management of Test Environments, like Deployments, Incidents & Change, and may also be responsible for managing other aspects of the testing process, such as tooling and test data.

The TEM role is often filled by a technical individual, perhaps originally a system or technical test engineer, with a good understanding of the development & test life cycle.

Note: In a large organization there may be many Test Environment Managers, either dedicated to a single Testing Environment, System, and/or a Business Division.

What is Test Environment Management (TEM)?

Definition: IT & Test Environment Management is the act of understanding your cross-life-cycle IT environments and establishing proactive controls to ensure they are effectively used, shared, rapidly serviced and provisioned, and/or deleted promptly.  

The key activities to consider when managing test environments are:

  • Know what your IT and Test Environments look like through Environment Modelling.
  • Capture Demand across Projects and Dev & Test Teams and avoid testing environment resource contention via Test Environment Bookings.
  • Support Change & Incident through IT Service Management (ITSM) requests/support ticketing.
  • Proactively Manage Testing Environment Events through collaboration with Calendars & Runbooks (Standard Operating Procedures).
  • Streamlining your IT Operations, and software development lifecycle, through investment in application, data & infrastructure automation. For example consider: Provisioning, Rollback, Decommissioning, and Shake Down scripts.
  • Deliver Insights on Structure, Usage, Availability, and Operational Capability. Ideally real-time through an enterprise-level Test Environment Management tool.
  • And finally, Improving continuously through Environment Housekeeping and Optimization.

What Test Environment Management Tools are available?

Test environment management tools help to support the creation and maintenance of effective test environments by providing a way to manage different aspects of the test environment. Test environment management tools can range from reservation and scheduling to infrastructure configuration and deployment. Using these tools, organizations can improve the efficiency and quality of their testing process, as well as reduce the associated costs.

There are a variety of TEM tools available, each with its strengths and weaknesses. To choose the right tool for your organization, it is important to first understand your specific needs and requirements. Once you have a clear understanding of your needs, you can then evaluate the different options and select the tool that best meets your needs.

Some of the most popular test environment management tools include:

Each tool has its unique features and pricing structure, so it is important to compare and contrast the different options before making a decision.

To Conclude

Test environment management is a critical part of the software development and testing process, and the right test environments and TEM people can make a big difference in the quality and efficiency of your IT delivery process. In addition, adopting the correct Test Environment Management Tool will help your software teams produce and maintain high-quality test environments, accelerate TEM operations and implement important Test Environment Management best practices.

What Is Test Data Management

Test Data Management! The Anatomy & five tools to use.

Test Data Management! The Anatomy & five tools to use.

Being part of the IT leadership in an organization has its advantages, but it also means you have to be familiar with technical “buzzwords”.

  • “Test Data Management” is one such term you might come across.

Do you know what it means and why it matters? And what about the available test data tools you can employ? If the answer to one or more of these questions is “no”, then this post is for you.

Let’s start by dissecting the expression into its various body parts. We’ll define each one and then reassemble the definitions. Once we’re done defining the term, we’ll get into the meat of the post by showing five existing test data tools that can help with test data management. Let’s get started!

What Is Test Data Management

Test Data Management: Breaking it Down

Let us break it down into its key components i.e. Test, Data & Management.

A definition of Testing.

Software testing is an investigation conducted to provide stakeholders with information about the quality of the software product or service under test. Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation. Test techniques include, but are not limited to, the process of executing a program or application with the intent of finding software bugs (errors or other defects).

A definition of Data.

Test data, unlike the sensitive data found in our production data, is any data that’s necessary for testing purposes. This includes test data for inputs, expected test data outputs, and test environment configuration details. Test data can come from a variety of sources, including production databases, synthetic data generators, and manual input.

A definition of Management

Management is the process of administering an organization, which can be a business, non-profit, or government body. This entails setting the organization’s goals and objectives and then coordinating the efforts of employees or volunteers to achieve these targets. The available resources that can be employed include financial, natural, technological, and human resources.

Bringing TDM Together

Now that we have the definitions for each word, it’s time to put all of them together to create a complete definition for “test data management.” Here it goes:

  • Test Data Management (TDM) is fundamentally test data preparation. It is the process of helping you prepare test data and maintain the test data in support of software testing. The goal of TDM is to provide a test environment that is as close to production as possible, and promotes data security while still being able to accurately test the software.

This may include, but is not be limited to underlying features like:

  • Test Data Profiling i.e. The Process of Discovery & Understanding your Data.
  • Test Data Preparation i.e. Generation of Realistic Test Data Using Automation to Fabricate Fake / Synthetic Data.
  • Test Data Security i.e. Using Production Data & Masking / Privacy Methods on the original production data. With the intent of ensuring “Personally Identifiable Information” (sensitive customer data) is removed and we prevent a data breach.
  • Test Data Provisioning i.e. Rapid Snapshotting, Cloning & Provisioning of Test Data/
  • Test Data Mining i.e. The ability to View and Access Valid Test Data.
  • Test Data Booking i.e. the ability to reserve Test Data for your engineering purpose

Here Are 5 Test Data Management Tools for Your Review

Here are five Data Generation Tools your organization can use to improve its approach to Test Data.

BMC (Compuware) File Aid

Compuware’s Test Data Management solution offers a standardized approach to managing test data from several data sources. Test Data Management with Compuware seeks to eliminate the need for extensive training by making it easy to create, find, extract, and compare data.

The solution can load subsets of related production data while maintaining database and application relationships. Test data management can help reduce the risk of errors, improve product quality, and shorten development timelines.

Broadcom (CA) Test Data Manager

Test Data Manager by Broadcom is a powerful test data management tool that enables organizations to manage their testing data more effectively and efficiently. Test Data Manager provides users with the ability to track, manage, and visualize their testing data in a centralized repository. Test Data Manager also offers features for managing test environments, managing test cases, and generating reports.

Enov8 Test Data Manager (DCS)

Enov8 Test Data Manager, originally known as DCS (Data Compliance Suite), is a Test Data Management platform that helps you identify where data security exposures reside, rapidly remediate these risks without error and centrally validate your compliance success. The solution also comes with IT delivery accelerators to support Data DevOps (DataOps), create test data, data mining, and test data bookings.

IBM InfoSphere Optim

IBM InfoSphere Optim is a tool that manages data at the business object level while preserving the relational integrity of the data and its business context. This allows you to easily create environments that precisely reflect end-to-end test cases by mirroring conditions found in a production environment.

InfoSphere Optim also offers other features such as data masking, ensuring data security, and subsetting, which can further help you reduce the risk of data breaches when testing in non-production environments.

Informatica Test Data Management

The test data management solution from Informatica, Test Data Management, is a tool that can identify ‘sensitive data,’ subset it, mask it, and create test data. It also allows developers and testers to save and share datasets to enhance overall efficiency.

Conclusion

As previously said, there are a lot of “buzzwords” in software engineering, and that trend isn’t going to change any time soon. Some of these words are simply fads. They seem like the “latest and greatest thing.” But just as quickly as the hip kids started using them, they fall out of favor.

However, Test data management isn’t one of those fads. It’s a process that your company must master and improve if it wants to stay competitive and promote values like Data Privacy. Test Data Management is essential in the understanding of data, it impacts our IT operations & project velocity & is key to our information security protocols.

In this article, we used a divide and conquer technique to define test data management. Test data management is the process of handling test data throughout the software development life cycle. Test data management tools help organizations manage this process by providing a way to store, track, and manipulate test data. There are many different test data management and data security solutions available on the market, each with its unique features and capabilities. So have a look & choose. Each is powerful and has its nuances. Look at the capabilities of each and decide which of the “Test Data Management” features are most important to you.

Author: Mark Dwight James

This post was written by Mark Dwight James. Mark is a Data Scientist specializing in Software Engineering. His passions are sharing ideas around software development and how companies can value stream through data best practices.

VSM Need for Speed

Test Environments: How to Value Stream DevOps With TEM

For many organizations, DevOps is the best practice for efficiency. However, this model doesn't come easily as the organization needs to put certain things in place. For example, the firm needs to incorporate the right tools to ensure its delivery pipeline and feedback loop are working as expected. Many firms get it all wrong when there's a problem in their delivery pipeline or feedback loop. This will cause issues for the firm as there's a loss of time and an overall reduction in efficiency.

To avoid an occurrence like this, firms need to ensure their DevOps model is efficient and adds value to customers. For these reasons, firms adopt the test environment management (TEM) model to check that their model works as expected. Sometimes, this may seem like a lot of work if not done correctly.

In this article, we will explore what test environment management is and how an organization can use it to measure and add value to a DevOps model. First, we'll define DevOps, the value stream, and test environment management. Then, we'll explain how and why you should value stream DevOps with TEM.

VSM Need for Speed

Defining Our Terms

To get us all on the same page, let's discuss DevOps, the value stream, and test environment management.

DevOps

A company's first priority should be satisfying their customers' needs. For software organizations, this involves shipping out features to end users as quickly as possible. To do this, software engineers make use of the DevOps model. DevOps consists of rules, practices, and tools that let the software engineering team deliver products to end users faster than traditional methods would allow. In conventional methods, the people responsible for a project's operation and the people responsible for a project's development are on distinct teams. This isn't the same for DevOps. In DevOps, development engineers and operations engineers work closely together throughout the application life cycle. This structure decreases handoffs, waiting time, and communication breakdowns to create a speedy development cycle.

The Value Stream

When developing or improving products for end users, companies need to understand what their customers really want. A company might add new features to their product, but the new features won't help them if they don't speak to the users' needs. Some features, if shipped to users, might reduce customer engagement with your product because they're not wanted or broken. It's discouraging to develop a feature tirelessly only to find out that users don't like it. How do you know that your features will please your customers? This is where the value stream comes into play. A value stream is the sequence of steps an organization takes to develop software. Ideally, each step in the development cycle adds value to the product. By analyzing their value stream, an organization can learn which development stages provide the most return on investment and which could be improved. For example, if your value stream includes a long wait time between building code and testing the code, you can guarantee that reducing the wait time between these stages will add value to your product. Value streams help the firm measure, monitor, and incorporate what will bring value to customers at the end of the day.

Test Environment Management

Before shipping new features or products to users, it's a good practice to test their functionality. Developers should know how responsive their application is from the perspective of a user. For example, you don't want a part of your product to be broken, unresponsive, or inaccessible. Such features will deter customers from using your product and may lead to negative reviews, which deter customers even more. To test software's functionality before shipping it to users, engineers make a test environment. A test environment is like a simulator: it allows you to imitate your application's operation and functionality. Basically, you're seeing your product and interacting with it as a user would. The testing environment has maturity levels, which are different protocols and practices depending on the state of your app you can follow when testing your application functionality. TEM consists of sets of procedures or guides that help developers create valid and stable test environments for their products. It allows you to control the test environment itself through things like communication, test cases, automation, bug detection, and triage. For example, you may want to test the overall responsiveness of your product. To do this, you first have to test the functionalities of smaller features. Next, you'll have to review product defects and implement measures for optimization.

Putting It All Together: Value Stream DevOps With TEM

Now that you know what DevOps, the value stream, and TEM are, it's time to learn how they can work together to help you innovate and delight your user base.

Focus on Time and Value

There are a lot of things to consider when shipping products to users. These can be summed up into time and value. Let's imagine a case where a firm ships a feature to users on time, but it's unresponsive. While time was met in delivering this feature, value wasn't. At the end of the day, you get unsatisfied customers who won't be happy at the firm's choice of feature. Another case is when the company doesn't ship features on time. When this happens, you get angry customers who don't seem to understand why it's taking your team so long to release new features. For software firms to really up their game, they have to ship features that add value to customers at the right time. However, the processes of DevOps, value streaming, and TEM will prevent these things from happening. These three methods create automatic checks in your software development cycle that stop you from pursuing projects customers won't like. And guardrails will keep you on schedule to deliver products in a timely fashion. This might sound complicated, but it's easy to get started.

How to Value Stream DevOps With TEM

In this section, we'll explore ways to ship features that add value to users at the right time through a combination of DevOps, value streaming, and test environment management. These are ideas for you to start devising your own DevOps–value stream–TEM strategy.

Logging and Testing

Often, it's difficult to aggregate logs during the developmental stage of a product. Most developers don't find out that the tools they use for logging don't aggregate logs properly until they're in the right test environment. For an application that depends hugely on logging and tracing, this may be a problem for users. Black box testing also doesn't allow developers to see the products from the customer's perspective. There could be bugs in the application's UI which may be overlooked. Some of these bugs cause unresponsiveness—which, as we discussed, can spell disaster for a product. All these can be mitigated when developers incorporate the right test environment.

Elimination of Redundant Procedure

Numerous firms make the mistake of incorporating redundant and wasteful processes in the development stage when there's no test environment management. Developers can fish out and eliminate redundant and wasteful procedures with test environment management. This will save the firm time and money, creating value for customers.

Visual Representation and Process Clarity

Visual representation and clarity are another way to value stream DevOps with TEM. Test environment management provides developers with a visual representation of each feature and how much value it adds to the product, thereby clarifying which elements are vital to a product's success and which could be improved.

Maturity Levels

Maturity levels tell the engineers the next step to take when testing a product. Policies are written for each step and every unit of the application tested. The engineer isn't testing the application by intuition or suspicion. Rather, there's a carefully planned guide on how to best test the application. It's imperative to understand and apply different maturity levels because it allows developers to measure the readiness of their test environment and define the process they'll use in test environments.

Feedback Loop

After shipping products to users with the DevOps model, there's a feedback loop. The feedback loop involves monitoring responses from users and incorporating that feedback as a feature in the next release. Feedback loops help developers determine what kind of feature and test environment they'll be working on and the type of test policies to write in the different maturity levels.

Integrate TEM and DevOps Seamlessly

DevOps remains one of the best models software engineers use to ship products to users. In this article, we have explored how engineers can ship products that add value to users at the right time with test environment management and value stream mapping. These practices give rise to several strategies for improving the time spent on features and value delivered by features, including logging and testing, eliminating redundancies, visually representing the product, assessing the feature's maturity, and creating a feedback loop. Test environment management can become an overwhelming task if you don't use the right tools and procedures. For example, there's the difficulty of choosing the right test environment and eradicating redundant procedures. You can integrate DevOps in the right test environment easily with test environment management resources from Enov8. These resources offer various tools like data sheets, visuals, case studies, and white papers to help integrate your DevOps model in the right test environment.

Author

This post was written by Ukpai Ugochi. Ukpai is a full stack JavaScript developer (MEVN), and she contributes to FOSS in her free time. She loves to share knowledge about her transition from marine engineering to software development to encourage people who love software development and don't know where to begin.

Measuring Test Environment Maturity

Measuring Your Test Environment Maturity

The goal of every company is to satisfy its users. This certainly applies in the software industry. However, as the number of users increases, they tend to make more demands. Increased demands will increase how complex software is, as these demands may require adding new features. And of course, software firms try hard to control defects in their products whenever they add a new feature.

Nevertheless, the industry is still far from zero defects. To avoid defects in products shipped to users, firms in the software industry must pinpoint defects in their test environment before shipping products to users.

What's a test environment, and how are developers making sure that they can find and cure defects in that environment? We'll discuss both topics in this article.

Measuring Test Environment Maturity

What Is a Test Environment?

A test environment is like a simulator that provides real-life visual representation. It includes a server that allows developers to run tests on their software.

A test environment also allows developers to include hardware and network configuration. The purpose of this is to let the test engineer mimic the production environment so that they can find defects. Also, test engineers can write custom tests and execute them in the test environment. This lets test engineers ensure that the software is responding as it ought to.

Let's look at how test engineers make sure their test environment mimics the production environment. When that happens, the team can remove issues and defects from software before shipping it to users.

What Is Test Environment Maturity?

Test environment maturity is a set of leveled guides that help test engineers determine how well-developed and rigorous their testing system is. Test engineers need to understand how the products they're about to test actually function. The engineers should also be able to define the process they'll use in test environments and manage those environments. And there are different levels of test environment maturity.

 

To understand test environment maturity better, let's look at the Test Maturity Model (TMM). We'll examine the different levels and find out how test engineers can measure environment maturity.

Test Maturity Model (TMM)

In order for test engineers to manage their test processes properly, the Illinois Institute of Technology developed the TMM framework. This framework works well with the Capability Maturity Model (CMM), which is the industry standard for software process development.

The TMM framework defines five maturity levels so that test engineers can manage their testing processes properly. These maturity levels help test engineers identify the next improvement state in their test environment.

Test engineers can't measure their test environment maturity if they don't know the level of maturity of their test environment. This is exactly what the TMM maturity level does. It displays levels of maturity and the steps required to attain each level.

Maturity Levels

Each maturity level consists of steps that are essential to attain test environment maturity. Let's look at the different TMM maturity levels and consider how test engineers can measure their test environment maturity.

1. Initial Level

In the first level in the TMM framework, the goal of the test engineer is to ensure that the software is running successfully. The goal here is simply to make sure that the software developers have developed a working product. Although TMM doesn't identify any process area for this level, the software should be working fine without breaking. So Level 1 has a low bar!

2. Definition Level

Definition is the second maturity level in the TMM framework. In addition to ensuring that the software is running successfully in the test environment, the test engineer needs to define test policies. This is because at this maturity level, basic testing methods ought to be in place. You're trying to answer the question, "Does the software do what it's supposed to?"

 

The different process area that this level identifies are:

  • Test policies and goals: This is to make sure that test engineers specify goals and policies they need to achieve.
  • Test methods, techniques, and environment that test engineers are using: It's essential to spell these out.

3. Integration Level

This level involves the integration of testing methods, techniques, polices, and environment defined in the definition level. It's necessary to do this so test engineers can determine software behavior. During the integration level, the engineers test life cycle and integration. Completing this step ensures testing is organized and carried out in a professional manner.

4. Management and Measurement Level

This TMM maturity level ensures that test engineers carry out quality test processes. At this stage, developers can evaluate and review software for defects. For example, after the integration level, the test engineers need to make sure they pick out all of the defects. The process areas this level identifies are test measurement, evaluation, and reviews.

5. Optimization Level

This is the final level. At this stage, the aim is to ensure that test processes and environment are optimized. This maturity level is important because testing isn't effective unless defects are controlled. In this level, the team members figure out how to prevent defects. The process areas in this level are test improvement, optimization, and quality control.

Best Practices in Measuring Test Environment Maturity

We've explored the different maturity levels for TMM and discussed how this model is the industry standard for software testing. In this section, we'll explore the best practices for measuring test environment maturity.

Hire a Test Engineer

A test engineer is in charge of carrying out tests on software to make sure it performs as expected. It's important to employ a test engineer to manage software testing. Why? Because a qualified test engineer is highly skilled in using the right test environment, techniques, and tools.

Understand the Test Maturity Model

When you employ a test engineer for your firm, make sure that they understand the test maturity model. This is because they can't measure what they don't understand! Fully understanding the test maturity model will enable the test engineer to determine which processes are covered in each level and precisely what level their test environment has gotten to.

Don't Skip Steps

It's a bad practice to skip or merge different levels of the maturity models. This will not only make software testing confusing, but it may also produce adverse test results. Therefore, direct test engineers to write down the maturity levels and proposed date of completion before beginning to test.

Automate Testing

When test engineers automate testing, it becomes easier and faster to measure test environment maturity. For example, this test environment and management tool from Enov8 allows test engineers to automate tests and manage test environments without a hitch.

Measuring Test Environment Maturity Goes Better When You Understand Test Environment Management

Knowledge of TMM maturity levels isn't enough to measure test environment maturity properly. To do so, test engineers need to be familiar with test environment management (TEM) and how it applies to TMM. So, let's explore TEM.

Test environment management, according to Enov8, is the act of understanding IT environments across the life cycle and proactively controlling them to ensure they're effectively used, serviced, and deleted promptly. With test environment management, test engineers can easily analyze software capability. This is because proper test environment management allows test engineers to measure test environment maturity properly. For this reason, there are tools like Test Environment Management Maturity index (TEMMi) to help firms understand test environment management.

Author

This post was written by Ukpai Ugochi. Ukpai is a full stack JavaScript developer (MEVN), and she contributes to FOSS in her free time. She loves to share knowledge about her transition from marine engineering to software development to encourage people who love software development and don't know where to begin.

What Is Data Virtualization

Data has undergone a huge shift from being an unimportant asset to being the most valuable asset a company holds. However, just holding the data doesn’t bring many benefits to your organization. To reap the benefits of the data your company collects, data analysis helps you to find valuable insights for the data you hold.

Data lays at the core of many important business decisions. Many companies prefer a data-driven decision-making policy because it greatly reduces guessing and helps the company to shift toward a more accurate form of decision-making. This greatly benefits the company as you have more trust in the choices you make and you can reduce the number of “incorrect” decisions.

For example, say a product company wants to know if users like the new feature they’ve released. They want to decide if they need to make further improvements to the feature or not. To make a more informed decision, the product company collects user satisfaction scores about the new feature. The company can then use the average user satisfaction score to make this decision. Data virtualization helps you to quickly aggregate data from this survey, as well as other important data that influences the decision, in a single, centralized view. This allows your business to make more informed decisions quicker.

This article introduces you to the concept of data virtualization and how it can help your company to make better decisions. Before we start, what are the common problems companies experience with data?

Common Data Problems for Organizations

Here’s a list of data challenges companies commonly experience:

  • It’s hard to understand the data you’ve collected.
  • Different sources of data use different formats, which makes it harder to retrieve insights.
  • Your organization experiences data lag, which means that data isn’t directly available.
  • Your organization isn’t ready to handle and process data. This could be due to, for example, missing data infrastructure and tools.

Now that you’ve read the above data problems, make sure your organization is ready to handle and process data. So what is data virtualization?

What Is Data Virtualization?

Data virtualization is a form of data management that aggregates different data sources. For example, a data virtualization tool might pull data from multiple databases or applications. However, it’s important to understand that it doesn’t copy or move any of the data. You can have multiple data silos.

Data virtualization is capable of creating a single, virtual layer that spans all of those different data sources. This means your organization can access data much faster since there’s no need to move or copy data. Furthermore, this is a major benefit as you can access data in real time. Virtualization improves the agility of the system, and companies can run analytics faster, gaining insights quicker. For many companies, being able to retrieve insights faster is a great competitive advantage!

As mentioned, data virtualization doesn’t copy or move any data. It only stores particular meta information about the different locations of the data that you want to integrate into your data virtualization tool.

What Is the Importance of Data Virtualization?

First of all, data virtualization acts as the pinnacle of data integration. It allows an organization to integrate many different data sources into a single data model. This means companies can manage all of their data from a single, centralized interface.

Moreover, data virtualization is a great tool for collecting, searching, and analyzing data from different sources. Furthermore, as there’s no data copying involved, it’s also a more secure way of managing your data since you don’t have to transfer the data.

In other words, data virtualization helps companies to become more agile and use their data faster, creating a competitive advantage as you receive analytics and insights more quickly.

What Are the Capabilities of Data Virtualization?

This section describes the capabilities of data virtualization and why they matter for your business.

  1. Agility
    A data virtualization tool allows you to represent data in different ways, format data, discover new relationships between data, or create advanced views that provide you with new insights. The options are endless. Agility is the most important capability of data virtualization as it decreases the time to a solution.
  2. High performance
    A data virtualization tool doesn’t copy or move any data. This contributes to its high-performance nature. Less data replication allows for faster data performance.
  3. Caching
    Caching frequently used data helps you to further improve the performance of your data virtualization tool. Whenever you query for data or a specific data view, part of the data is already cached for you. This puts fewer constraints on your network and improves the availability of your data.
  4. Searchability
    A data virtualization tool allows you to create data views that provide you with actionable insights. Furthermore, data virtualization provides you with a single, centralized interface to search your data.

Next, let’s explore the benefits of data virtualization for your organization.

What Are the Benefits of Data Virtualization?

Here are 10 important benefits of employing a data virtualization tool for your organization.

  1. Helps with hiding the data complexity from the different underlying data sources, data formats, and data structures.
  2. Avoids replication of data to improve performance.
  3. Gives real-time data access and insights.
  4. Provides higher data security as no data is replicated or transferred.
  5. Reduces costs since no investments are needed in additional storage solutions.
  6. Allows for faster business decisions based on data insights.
  7. Reduces the need for development resources to integrate all different data sources.
  8. Allows for data governance to be applied efficiently. For example, data rules can be applied with a single operation to all different data sources.
  9. Improves data quality.
  10. Increases productivity as you can quickly integrate new data sources with your current data virtualization tool.

Now that we have a better understanding of the benefits of data virtualization, it’s time to get serious. The next section explains how you can implement data virtualization in your organization.

How to Get Started With Data Virtualization

Do you want to get started with data virtualization for your organization? The most important tip is to start small. Assign a dedicated team who spends time on integrating one or a couple of data sources. Start with data sources that are most valuable for your organization. This way, you’ll see the benefits of data virtualization quickly.

Next, when your team has completed some simple data integrations, it’s time to scale up your operations and use the tool for most of your data sources. You can think about more complex data models, integrate complex data sources, or use data sources with mixed data types.

Furthermore, you can start to experiment with caching to see where it can be applied effectively to gain the most performance benefits. Remember to apply caching to frequently used data or data models.

As a general rule of thumb, prioritize high-value data sources to reap the most benefits.

Conclusion

One final note: data virtualization isn’t the same as data visualization. The two terms are often used interchangeably, but they have very different meanings. Data virtualization isn’t focused on visualizing data. The main goal of data virtualization is to reduce the effort of integrating multiple data sources and providing your organization with a single, centralized interface to view and analyze data.

In the end, the real business value of data virtualization lays in the agility and faster access to data insights. For many organizations active in the industry of big data or predictive analytics, it’s a real competitive advantage to access insights faster than your competitors. This allows you to make profitable decisions faster than the competition.

If you want to learn more, the following YouTube video by DataAcademy further explains the concept of data virtualization in easy-to-understand terms.

Author

This post was written by Michiel Mulders. Michiel is a passionate blockchain developer who loves writing technical content. Besides that, he loves learning about marketing, UX psychology, and entrepreneurship. When he's not writing, he's probably enjoying a Belgian beer!

Comparing Configuration and Asset Management

When you’re running an IT organization, it’s not just the business that you have to take care of. One part of running a business is building, creating, and providing what your customers need. The other part is management. Out of all the things you have to manage, configurations and assets are two of the most important.

Although people often think of configuration management and asset management as the same thing, but they are different. People also sometimes confuse these terms with each other. So, in this post, I’ll explain what configuration management and asset management are and how they’re different. Let’s start by understanding each of these terms.

What Is Configuration Management?

Configuration management is the management of configuration items. So, what are configuration items?

Configuration Items

Any organization provides certain services. These services might be the ones being provided to customers or to internal users. Either way, creating and providing these services requires some components. So, any component that needs to be managed to deliver services is called a “configuration item.”

Too confusing? No worries—I’ll explain with an example. Consider that you’re providing a service that tracks an organization’s user data. In this case, you can consider the software to be the component that needs to be managed. It’s important that you manage this software to make sure your service works fine. This means that your software is a configuration item. Another way of defining a configuration item is that it’s a component that’s subject to change to make the service delivery better.

What Information Is to Be Managed?

When you manage the attributes of such configuration items, that’s configuration management. So, what kind of information do you have to manage? You have to manage attributes such as ownership, versioning, licensing, and types. Let’s consider an example in which you’re using software for internal tasks.

Now you’ve identified that the software that provides service is your configuration item. The next step is to manage information related to that software. The software developer will have released different versions of the software with updates and new features. You obviously look out for better versions of the software or the version that best suits your requirements. One piece of information that you have to manage is the details of the software versions.

Another example is when you’re using licensed software. The software will be licensed to a particular person or company, and the license will be valid for a certain period of time. Such information becomes the attribute you have to manage. Now that you know what configuration management is, let me tell you a little about how it’s done.

Configuration Management Database

An easy way to manage information on configuration items is by using a configuration management database (CMDB). A configuration management database is just like any other database that stores data, but it specifically stores information related to configuration items.

Configuration Management System

Configuration management isn’t easy. You have to take care of lots of tasks, such as tracking the data and adding and modifying configuration items. To make configuration management easy, you can use a configuration management system (CMS), which is software that helps you manage your configuration items. A typical CMS provides functions for storing and managing CI data, auditing configuration, making changes to the configurations, and so on.

Now that you know what configuration management is, let’s talk about asset management.

Asset Management

In generic terms, anything that’s useful is an asset. If you own a house or a property, that’s an asset for you. So is your car or your phone. When it comes to an organization, anything that’s useful to the organization is an asset. Assets can be capital, office property, the servers locked in your highly secured server room, and so on. But IT assets aren’t limited to physical or material things. The knowledge stored in your employees’ brains is also a valuable asset to your organization.

So, basically, tracking and managing the assets of your organization throughout its life cycle is asset management. The main aim of asset management is to create processes and strategies that help in managing assets properly. The asset management process starts right from the moment of acquiring the asset until disposing of the asset.

For example, let’s say you have an organization that builds and manages web applications. As part of this, you own some servers that you host the web applications on. You also have some databases where you store data for your clients. In this case, your asset management process starts from the time you bought the servers and the databases. You have to manage the buying, maintenance, and inventory costs. Along with that, you also have to take care of regular updates, audits, security implementations, and any changes that you make. This asset management goes on either until the assets are damaged or until they stop being useful to your organization and are disposed.

Asset management directly involves finance. You have to consider the inventory, governance, and regulatory compliance along with the financial aspects in asset management.

Why Do You Need Asset Management?

Asset management helps you understand your financial flow and how to efficiently plan your finances. You can easily track your asset throughout its life cycle. This helps you analyze incidents if something went wrong. Management of assets improves your assets’ quality and performance, which helps your business.

The asset management process helps you stay compliant with various rules and regulations. This improves the quality of your business and also saves you money on audits and fines. Because asset management lets you track your assets, you can plan more efficient strategies for operations.

Configuration Management vs. Asset Management

Now that I’ve explained each of these terms, I hope you understand what they mean. At some point, you might have felt that they were the same. To eliminate any lingering confusion, let me highlight the differences between them.

Asset management is managing anything valuable to your organization. You can consider configuration management to be part of asset management. Configuration management mainly focuses on managing configuration items and their attributes. These attributes mainly affect the delivery of the service.

In the case of asset management, it’s more of a financial perspective. You track the asset to understand the financial flow and need for that asset throughout its life cycle.

To understand the difference, let’s take an example of a hardware component that you’re using—let’s say, a database. When you’re using a database, the database itself becomes an asset. You have to manage the maintenance, track the asset, conduct audits, and so on. This is asset management. The same database will have software versions. Keeping track of the software version, updating it, and tracking which other components it works with becomes part of configuration management.

Configuration management and asset management might sound the same at a high level, but they have different purposes and are implemented differently. Understanding such terms with the help of an example really makes it easy to understand the differences, hopefully, the explanations and examples here have helped you.

Author

This post was written by Omkar Hiremath. Omkar uses his BE in computer science to share theoretical and demo-based learning on various areas of technology, like ethical hacking, Python, blockchain, and Hadoop.

DevOps Tool CHain

What Is a DevOps Toolchain and Why Have One?

DevOps is not a technology, it’s an approach. Though there’s flexibility in how to use it, there’s also the added responsibility of using it in the best possible way. The whole idea of DevOps is to make the software development process smoother and faster. And one of the most important decisions needed to achieve this is to decide on the right toolchain.

So in this article, I’ll tell you what a DevOps toolchain is and why you should have one.

What Is a DevOps Toolchain?

The whole DevOps practice stands on two main pillars: continuous integration and continuous delivery. This means that the changes and upgrades to a product must be integrated at greater frequency, and they should be available to the users at greater speed. A DevOps toolchain is a set of tools that helps you achieve this. But why are multiple tools needed? Why not just use one? That’s because DevOps is a practice that has different stages. To help you understand this, I’ll take you through the different stages of a software development pipeline that’s based on a DevOps approach and review what tools you can use.

DevOps Tool CHain

Planning

The first step of doing anything is planning, and that holds true for DevOps as well. Planning includes the personnel inside the organization as well as the clients. Both need to have a clear understanding of what they want to build and how they are going to do it. Therefore, transparency plays an important role. You can use tools like Slack, Trello, and Asana for the planning stage.

Collaboration

The beauty of DevOps is that it requires multiple teams to collaborate and work together for efficient software delivery. Once the planning is done, you need to focus on collaboration. Collaboration happens between people from different teams, who might have different working styles or live in different time zones. Easy collaboration requires transparency and good communication. Some of the tools available for collaboration include Slack, Flowdock, WebEx, and Skype.

Source Control

Source control aka version control means managing your source code. In DevOps, where there are frequent updates to the source code, it’s important that you handle it carefully. This means you need a tool that can manage the source code and make different branches available as required, especially when multiple teams are working on a single product. Some of the most popular source control tools are Git and Subversion.

Tracking Issues

You should also be ready for issue occurrence. And when it comes to issue handling, tracking the issue plays an important role. Issues should be tracked in a transparent way that provides all the necessary details required to properly resolve them, and improved tracking results in faster resolution. You might want to consider using tools like Jira, Zendesk, Backlog, and Bugzilla.

Continuous Integration

This stage, as mentioned earlier, is one of the most important parts of the DevOps practice. This is the stage where modular code updates are integrated into the product to make frequent releases. It’s commonly known to developers that the code doesn’t always work smoothly when it makes it to production. You need a tool that helps with easy integration, detecting bugs, and fixing them. Jenkins, Bamboo, Travis, and TeamCity are some of the most popular tools.

Configuration Management

When developing a product, you will have to use different systems. Configuration management tools help you in maintaining consistency across systems by configuring all the systems automatically for you. They basically configure and update your systems as and when required. The configuration management tools that are heard of quite often are Ansible, Puppet, and Chef.

Repository Management

DevOps teams work together to release updates as soon as possible, and when multiple teams are working on them, there will be an update every day or maybe even every hour. With this frequency, it’s important to have a tool that manages binary artifacts and metadata. The repository management tools help push the product or a part of the product from the development environment to the production environment. Some well-known tools for repository management are Nexus and Maven.

Monitoring

Monitoring helps you understand how good or bad the release was. When there are frequent updates to your product, you can’t expect every release to perform well. Sometimes certain releases break the product, create security issues, decrease the performance, or bring down the user experience. The best way to understand what your update has resulted in is by monitoring it. Monitoring tools help you decide whether your release needs aid or not. You can use tools like Sensu, Prometheus, or Nagios.

Automated Testing

You’d for sure want to test your code before making it available to the users. When continuous delivery is the goal, manual testing would slow down the process. Automated testing makes the testing process faster because the tool does the testing, and the computer is faster than a human being. Also, there is no chance of human errors. But you have to make sure that the automated testing tool you choose is efficient and reliable because you cannot afford to have any mistakes here. A few tools you can choose for automated testing are QTP and TestComplete.

Deployment

This is the stage that actually delivers your product and its updates to the end users, and there are a few things that may go wrong here. The main purpose of deployment tools is to make continuous and faster delivery possible. Some of the most popular tools used for deployment are IBM uDeploy and Atlassian Bamboo.

Now that you understand what a DevOps toolchain is and which are some of the most used tools in the industry, let’s understand why it’s important to have a DevOps toolchain.

Why You Should Have a DevOps Toolchain

A DevOps toolchain is needed to maximize the positive outcome of DevOps practice, and it’s achieved when you choose your toolset wisely. A wisely chosen DevOps toolchain will show how the DevOps approach helps you build high-quality products with fewer errors and enhanced user satisfaction.

The first advantage of using a DevOps toolchain is that it decreases the defects and increases the quality of your products. Because of features like automated testing and error-checking deployment tools, there is also less room for errors. This is good for your business and the reputation of your company.

The second advantage is that a DevOps toolchain helps you innovate your product faster. Because the toolchain results in faster planning, building, testing, and deploying, you have more opportunities to innovate. The more innovative your product is, the more business you get.

The final advantage is related to incident handling. The toolchain helps you identify and manage major incidents. Doing so facilitates finding solutions to the incidents faster and letting the respective team know about the incident. This helps improve the support and quality of the product.

In Conclusion

Now that you’ve read about what the DevOps toolchain is and why you need it, it’s time to choose which ones are right for you. Even though I’ve mentioned a number of tools for various purposes, the ones you pick will differ based on what best suits your use case. There’s no universal toolchain that works best for everyone. You’ll know what’s best for you only after you understand your requirements and then choose the tools accordingly.

Author

This post was written by Omkar Hiremath. Omkar uses his BE in computer science to share theoretical and demo-based learning on various areas of technology, like ethical hacking, Python, blockchain, and Hadoop.

Why Test Data Management Is Critical to Software Delivery?

Imagine you are developing a system that will be used by millions of people. In a situation like this, a system has to be very well-tested for any type of error that can cause the system to break while in production. But what’s the best way to test a system for any possible system failure because of bugs? This is where test data management comes in.

In this post, I will explain why test data management is critical in software delivery. To develop high quality software products, you have to continuously test the system as it’s being developed. Let’s dive in straight to understanding how this problem can be solved by using test data management.

 

What Is Test Data Management?

Well, in simple terms, test data management is the creation of data sets that are similar to the actual data in the organization’s production environment. Software engineers and testers then utilize this data to test and validate the quality of systems under development.

Now, you might be wondering why you need to create new data. Why not just use the existing production data? Well, data is essential to your organization, so you should protect it at all costs. That means developers and testers shouldn’t have access to it. This has nothing to do with the issue of trust but security. Data should be highly regarded, or else there can be a data breach. And as you know, data breaches can cause loss to an organization.

How Can You Create Test Data?

So, now that we know why we need test data that is separate from our production data, how can we create it?

The first thing you must do is understand the type of the business you are dealing with. More specifically, you need to know how your software product will work and the type of end users that will use the software. By doing so, it will be easier to prepare test data. Keep in mind that test data has to be as realistic as the actual data in the production environment.

You can use automated tools to generate test data. Another way of creating test data is by copying or masking production data that your actual end users will use. Here you have to be creative as well and create different types of test data sets. You can’t rely only on the masked data from production data for testing.

Benefits of Test Data Management in Software Delivery

Test data management has many benefits in software delivery. Here are some of the benefits of test data management in software delivery in any software development environment.

High Quality Software Delivery

When you apply test data management to software delivery, it will give software developers and testers to test the systems and make solid validations of the software. This enhances the security of the system and can prevent possible failures of the system in the production environment. Testing systems with test data gives assurance that the system will perform as expected in the production environment without defects or bugs.

Faster Production and Delivery of Software Products to the Market

Imagine that, after some months of hard work of developing a software application, you’ve just released a software application on the market, only for it to fail at the market level. That’s not only a loss of resources, but it’s also a pain.

A system that’s well-tested using test data will have a shorter production time and excel at the production level. That’s because it’s much more likely to perform the way it was intended to. If the system fails to perform in production because it was not tested well, then the system has to be redone. This wastes time and resources for the organization.

Money Needs Speed

Test data management is critical when it comes to software delivery speed. Having data that’s of good quality and is similar to production data makes development easier and faster. System efficiency is cardinal for any organization, and test data management assures that a system will be efficient when released in production. Therefore, you start generating revenue as soon as you deploy the system.

Imagine having to redo a system after release because users discover some bugs. That can waste a lot of time and resources, and you may also lose the market for that product.

Testing With the Correct Test Data

Testing with good quality test data will help in making sure that the tests you run in the development phase will not change the behavior of the application in the production phase. For example, you might test that the system is accepting supported data by entering a username and password in the text box with all types of data that a user can possibly input into the system.

No matter how many times you test the software, if the test data is not correct, you should expect the software to fail in the production phase. This is why it is always important to ensure that test data is of great quality and resembles your actual production data.

Bug and Logical Fixes

How can you know that the text box is accepting invalid input such as unsupported characters or blank fields from users? Well, you find out by validating the system through testing.

The whole point of having test data in software delivery is to make sure that the software performs as expected. Additionally, you need to make sure that the same tests will pass in production and have no loopholes that could damage the organization’s reputation. Therefore, test data becomes a critical part of software delivery life cycle, as it helps to identify errors and logical problems in the system. Thanks to this, you can make fixes before releasing the software.

For example, imagine a loaning system that makes incorrect calculations by increasing the interest rate by a certain percentage. That can be unfair to the borrowers and can backfire for the lending company.

Earning Trust

Trust is earned, and if you want to earn it from the end users or management, you have to deliver a software product that’s bug-free and works as expected. In fact, every software development and testing team should utilize test data management. Test data management enables teams to deliver software products that stand out and earn trust from management. After all, you can’t ship an error-prone system to the market and expect happy users.

Why Test Data Management Matters

Well, the whole essence of test data management is to make sure that you test the software in all scenarios, ensuring that the software will not fail in production.

By testing with data that’s as realistic as production data, you gain assurance that the software application will function as expected in a production environment. This strengthens the organization’s relationship with clients that will be using the system because it will have fewer bugs.

Another benefit is the speed of software delivery. Test data management speeds the time of production because testing takes place as the software is developed. That way, you detect errors at an early stage of the software development life cycle and fix them before the release.

This reduces the chances of fixing bugs in production and rollbacks. The earlier you detect bugs, the easier and cheaper it is to fix those bugs. This also helps the organization’s compliance and security risks.

Test data management also reduces costs, as it speeds up the process of the software development life cycle. Money needs speed, and the market is always changing. Without test data management, bugs might delay the release of your software product. As a result, you might end up releasing the software only when it is out of market demand.

Summary

In simple terms, test data is simply the data used to test a software application that’s under the software testing life cycle. Test data management, on the other hand, is the actual process of administering data that’s necessary for use in the software development test life cycle.

You can’t deny that test data management is an essential part of testing and developing software. It plays a crucial role in helping you produce high quality software that’s bug-free and works as expected.

You should take test data management seriously and apply it when delivering software. If you do so, your organization will gain more revenue because you’ll deliver higher quality software products. Higher quality products make the customers happy instead of giving them a reason to complain about some bug.

Author

This post was written by Mathews Musukuma. Mathews is a software engineer with experience in web and application development. Some of his skills include Python/Django, JavaScript, and Ionic Framework. Over time, Mathews has also developed interest in technical content writing.

What are Test Data Gold Copies

What Are Data Test Gold Copies and Why You Need Them

You lean back in your chair with a satisfied grin. You did it. It wasn’t easy, but you did it. You diagnosed and fixed the bug that kept defying your team. And you have the unit tests to prove it.

The grin slowly fades from your face as you realize that you still need your code to pass the integration tests. And you need to get data to use in them. Not your favorite activity.

You can put that grin back on your face because there is another way: using a gold copy.

Read on to learn what a gold copy is and why you want to use one. You will also find out how it can help you work on an application with low test coverage. You know, the dreaded legacy systems.

What Is a Gold Copy

In essence, a gold copy is a set of test data. Nothing more, nothing less. What sets it apart from other sets of test data is the way you use and guard it.

  • You only change a gold copy when you need to add or remove test cases.
  • You use a gold copy to set up the initial state of a test environment.
  • All automated and manual tests work on copies of the gold copy.

A gold copy also functions as the gold standard for all your tests and for everybody testing your application. It contains the data for all the test cases that you need to cover all the features of your product. It may not start out as comprehensive, but that’s the goal.

Building a comprehensive gold copy isn’t easy or quick. But it’s definitely worth it, and it trumps using production data almost every time.

Why You Don’t Want to Test in Production

Continuous delivery adepts rave about testing in production. And yes, that has enormous benefits. However:

  • It requires the use of feature toggles to restrict access to new features and changed functionality.
  • Running the automated tests in your builds against a production environment is not going to make you any friends.
  • The sheer volume of production data usually is prohibitive for a timely feedback loop.
  • Giving developers access to production data can violate privacy and other data regulations.

There’s more:

  • Production data changes all the time, and its values are unpredictable, which makes it unsuitable as a base for automated testing.
  • Finding appropriate test data in production is a challenge. Testing requires edge cases, when users and thus their data tend to be much more alike than they would like to know.
  • To comply with privacy and other data regulations, extracts need to be anonymized and masked.

Contrived Test Data Isn’t Half as Bad as It Sounds

Contrived examples usually mean that you wouldn’t encounter the example in the real world. However, when it comes to testing, contrived is what you want. A contrived set of test data:

  • has only one purpose—verifying that your application works as intended and expected and that code changes do not cause regressions
  • contains a limited amount of data, enabling a faster feedback loop even for end-to-end tests
  • can be made to be self-identifying and self-descriptive to help understand what specific data is meant to test
  • contains edge cases that willtrip you up in the real world but are generally absent from production data by their very definition
  • can be built into a comprehensive, optimized, targeted set of data that fully exercises your application

Of course, production data can be manipulated to achieve the same. But extracting it stresses production, and manipulating it takes time and effort. And you really don’t want to be doing that again and again and again.

That’s why you combine contrived data and gold copies. You start your gold copy with an extract from production data that is of course anonymized and otherwise made to conform to privacy and data regulations. Over time, you manipulate it into that optimized, targeted set of data. But using that initial set of test data as a gold copy will bring you benefits immediately.

Benefits of Gold Copies

In addition to the benefits of contrived data, using a gold copy gets you these benefits:

  • You can easily set up a test environment with a comprehensive set of test data
  • You can easily revert the data in a test environment to its original state
  • The ability to automate spinning up test environments
  • Automated regression testing for legacy systems

Everyone working on your application will appreciate it. They no longer have to hunt for good data to use in their test cases. And they no longer have to create test data themselves. A good thing, because creating test data and tests that produce false positives (i.e., tests that succeed when they should fail) is incredibly easy. You only have to use the same values a tad too often.

The ability to automate spinning up a test environment is what makes using a gold copy so invaluable for large development shops and shops that need to support many different platforms. Just imagine how much time and effort can be saved when providing teams and individuals with comprehensive, standard test data that can be automated. For example, using containers and a test data management tool like Enov8’s.

Finally, gold copies can help reduce the headaches and anxiety of working with legacy code. Here’s how.

Slaying the Dreaded Legacy Monster

Any system that does not have enough automated unit and integration tests guarding it against regressions is a legacy system. They are hard to change without worrying.

The lack of tests, especially the lack of unit tests, allowed coding practices that now make it hard to bring a legacy system under test. Because bringing it under test requires refactoring the code. And you can’t refactor with any confidence if you have no tests to tell you if you broke something.

Fortunately, a gold copy can bail you out of this one. It allows you to add automated regression testing by using the golden master technique. That technique takes advantage of the fact that any application with value to its users produces all kinds of output.

Steps in the Golden Master Technique

How you implement the golden master technique depends on your environment. But it always follows the same pattern, and it always starts with a gold copy.

  1. Use your current code against the gold copy to generate the output you want to guard against regressions. For example, a CSV export of an order, a PDF print of that order, or even a screenshot of it.
  2. Save that output. It’s your golden master.
  3. Make your changes.
  4. Use your new code against the gold copy to generate the “output under test” again.
  5. Compare the output you just generated to your golden master.
  6. Look for and explain any differences.

If you were refactoring, which by definition means there were no functional changes, the comparison should show that there are no differences.

If you were fixing a bug, the comparison should show a difference. The golden master would have the incorrect value, while the output from the fixed code would have the correct value. No other differences should be found.

If you were changing functionality, you can expect a lot of differences. All of them should be explicable by the change in functionality. Any differences that cannot be explained that way are regressions.

Explaining the differences requires manual assessment by a human. It’s known as the “Guru Checks Output” anti-pattern. And it needs to be done every test run if you want to stay on top of things. Marking differences as expected can help. Especially when you can customize the comparison so it won’t report them as differences.

Go Get Yourself Some Gold

Now that you know what a gold copy is and how you can use it to your advantage, it’s time for action. It’s time to start building toward the goal of a comprehensive set of test data and use it as a gold copy.

Your first step is simple: save the data from the test environment you set up for the issue or feature you’re working on now. That is going to be your gold copy. If your application uses any kind of SQL database, you could use that to generate a DML-SQL script that you can add to a repository.

Use your gold copy to set up the test environment for your next issue. Make sure you don’t (inadvertently) change your gold copy while you’re working on that issue. When you’re finished, and if you needed to add test data for the test cases of this issue, update your gold copy.

Rinse and repeat, and soon enough you’ll be well on your way to a truly useful comprehensive set of test data.

Author: Marjan Venema

This post was written by Marjan Venema. Marjan's specialty is writing engaging copy that takes the terror out of tech: making complicated and complex topics easy to understand and consume. You’ll find samples on her portfolio. Her content is optimized for search engines, attracting more organic traffic for small businesses and independent professionals in IT and other Tech industries, that she’ll also help with content audits and strategy.