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!

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.