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.

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.

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!