Test Environment Management: A Best Practices Guide

Maintaining efficient test environments throughout the software development life cycle is essential for guaranteeing software quality. The manual management and configuration of test environments that are prevalent in traditional approaches lead to inconsistencies across different test environments and are resource- and time-intensive. This challenge is further amplified when conducting manual/automated testing. All of these tests require realistic testing environments that accurately reflect production conditions. Effective test environment management offers a solution: automating and optimizing the creation and management of test environments.

This article explores best practices for managing test environments, enabling developers to focus on their core competency: writing code. We also explore key concepts for establishing reliable and streamlined testing processes.

Summary of key test environment management best practices

The table below summarizes four test environment management best practices for streamlining and optimizing the testing process within the software development pipeline. In the sections that follow, we explore these best practices in more depth while focusing on isolated test environments, automation techniques for streamlining test setup and teardown, and ideal approaches for managing test data and configurations.

Use isolated preview environments (ephemeral environments)

Ephemeral environments are temporary, isolated, disposable environments that are used to provide a safe place for developers to experiment without affecting other parts of the system, conduct tests, or simply present a demo to stakeholders. This isolation is paramount for conducting both functional and non-functional tests for a number of reasons:

• Safe and controlled interaction: These environments provide a separate space to test how different modules or microservices interact. You can spin up individual environments for each integration point, ensuring no interference from other tests or development activities.
• Fast feedback loops: Ephemeral environments enable rapid test execution and feedback loops. Developers can quickly deploy changes, run integration tests, and identify issues early on before they propagate to other environments.
• Shared previews for collaboration: Isolated test environments can be shared efficiently with different stakeholders. Developers can collaborate and gather feedback on new features before merging them into the main codebase. Similarly, testers and designers can perform usability and user acceptance testing in a controlled environment, identifying user experience issues early. Tools like cloud-hosted shells or dedicated UAT access portals can streamline this process.

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Automate test environment management within CI/CD

Integrating automated test environment management within your CI/CD pipeline improves testing efficiency significantly. This automation can encompass tasks like environment provisioning, configuration, and teardown. Automation ensures that environments are set up and configured identically each time, leading to consistent and repeatable test results, reducing the risk of regressions. This way, developers can spend more time writing code and less time managing test infrastructure, improving time to market.

Test environment management also plays a pivotal role in facilitating the efficient and effective execution of both functional and non-functional tests.

Test automation using test environment management tools

Test environment management tools facilitate on-demand access to test environments through cloud-hosted shells or dedicated UAT access portals, streamlining the testing process. In addition, frameworks such as Cypress or Playwright can be seamlessly integrated with test environment management workflows through custom YAML files.

Test scripts written in Playwright or Cypress can leverage the isolated environments to conduct end-to-end tests in a controlled and automated manner. Testers can define user journeys and acceptance criteria within these scripts, enabling automated execution of certain scenarios (such as a happy path test or specific modules like user authentication). This streamlines the testing process and improves the consistency and repeatability of end-to-end and integration tests.

The availability and repeatability of these isolated environments remove the bottleneck of a shared testing environment that must be used for various types of tests. This allows multiple developers and teams to conduct integration testing, performance testing, load testing, and other types of tests without competing for the same resources.

Coherence's customizable CNC foundation lets you integrate any technology or framework.  Below is a YAML file of a Next.js app example for deploying a single-page app.

services:
  frontend:
    command: ["npm", "run", "export"]
    x-cnc:
      type: frontend
    build:
      context: .

Seed test databases

Populating test databases with realistic data is crucial for comprehensive testing. Seeding involves injecting the database with sample data that mirrors production data, which ensures that tests interact with data closely resembling what they will encounter in a real-world setting.

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Data seeding techniques

There are several techniques for seeding test databases:

• Static seeding: This involves predefining a fixed set of test data injected into the database during environment provisioning.
• Dynamic seeding: Here, the test data is generated dynamically based on predefined rules or scripts.
• Masking production data: In some cases, anonymized or masked production data can be used for testing.

Here are a few popular masking techniques that can be used:

• Tokenization: This method replaces personally identifiable information (PII) like names and social security numbers with randomly generated tokens (such as alphanumeric strings). Tokens offer no inherent meaning and cannot be easily reversed back to the original data.
• Pseudonymization: While similar to tokenization, pseudonymization replaces PII with fictitious but realistic data that retains a similar format (e.g., generating fake names with a similar name structure). This approach can be useful for testing scenarios involving user interactions without compromising actual user information.
• Data shuffling: Sensitive data points within a record (e.g., zip codes within a dataset) can be shuffled or randomized while maintaining the overall data structure. This technique preserves data distribution for analytical purposes but removes the direct association of the data point to a specific individual.
• Data redaction: This technique involves completely removing sensitive data fields from the dataset. It’s a good option for data points that are not crucial for testing functionality.

Benefits of data seeding

There are two main benefits of data seeding:

• Comprehensive testing: By utilizing data that reflects the variety and complexity of real-world data, a wider range of test cases can be covered. Seeding also allows for testing edge cases and corner scenarios that might not be captured with simple static data.
• Automation and streamlined testing: Data seeding scripts can be integrated with your CI/CD pipelines. This allows for automatic data seeding as part of the testing process, ensuring that each build or code change is tested with a consistent and up-to-date set of data. It also eliminates the need for developers to manually create or populate test data before each test run. This saves time and reduces potential errors caused by manual data entry. Following the test run, the CI/CD pipeline typically incorporates a teardown phase, which should include tasks like clearing the seeded data from the test database (which, in turn, prevents database bloating).

Another thing to consider is creating a test data repository for storing test data centrally in shared storage. This allows different teams to access the same set of data, ensuring uniformity in testing environments. This central repository also allows for version control of test data, enabling teams to track changes and maintain consistency over time. This makes troubleshooting and debugging test failures a lot easier, since the root cause can be isolated to code changes rather than variations in the test data.

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Considerations for choosing a data seeding technique

When choosing a test data seeding approach, consider these three factors:

• Data complexity: The right level of data complexity depends on the level of detail and variation required in your test data. Static seeding is suitable for simpler scenarios where a fixed set of data points is sufficient. Dynamic data seeding is ideal for complex data structures or scenarios requiring diverse data sets. Masking production data is useful for replicating real-world data complexity.
• Data security: This mostly applies to organizations that need to be compliant with specific industry standards when testing with user data. Masking production data requires the most caution, then comes dynamic data seeding. Ensure that you do not generate or expose personally identifiable information (PII) in your tests. Static seeding is generally less risky due to the fact that test data is predefined and controlled.
• Test scenario requirements: Finally, test scenario requirements also play a role in helping you choose your data seeding method. The right approach depends on how complex your test scenarios are. That being said, it’s important to note that you are not necessarily confined to one choice—you can combine techniques to meet your needs.

Example

Suppose you are testing the recommendation engine of an ecommerce website. To effectively do so, you need a diverse set of user data with varying purchase histories and browsing behaviors. You may want to use this approach:

• Dynamic seeding: Scripts can be used to generate a large number of test user profiles with diverse purchasing habits and browsing activities. This might involve simulating different product categories viewed, items added to the cart, and past purchases. It’s crucial to ensure that these scripts generate realistic and varied user data to accurately reflect real-world user behavior during testing.
• Masking production data: In addition to dynamically generated user data, a limited amount of anonymized production data with masked purchase history details can be used to supplement the test data. This approach leverages the real-world nature of user behavior patterns while protecting user privacy by removing any personally identifiable information.

In contrast, testing a payment form follows a completely different approach. Here, a predefined pattern of credit cards with specific characteristics (valid numbers, CVVs, and expiration dates) can be used to verify form functionality. These credit card numbers are typically nonfunctional and wouldn’t be processed by a real payment gateway. This approach is sufficient because the goal is to test the form’s ability to handle valid credit card formats and data submission, not to simulate real-world transactions.

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Choose appropriate tooling

Selecting the right tools for test environment management is critical. Consider solutions that offer features like these:

• Centralized management: Tools that offer centralized management of multiple test environments across different services hosted on cloud providers like AWS or GCP simplify oversight and control.‍
• Hosted infrastructure: This capability eliminates the need for on-premises machines and saves developers time on configuring and maintaining those resources.‍
• On-demand access: Some solutions provide on-demand access to test environments through cloud-hosted shells. These shells act like preconfigured Secure Shell (SSH) clients equipped with appropriate role-based access control (RBAC) and preloaded SSH keys for secure access. These shells can be accessed instantly through a web browser or command-line tools from anywhere with an internet connection. For instance, a developer who needs to test a bug fix in a specific integration point can access an isolated test environment (preconfigured with the necessary dependencies and access controls) provided by a test environment management tool. The developer can connect to this environment using a web browser or command-line tool with their preloaded SSH keys and begin testing immediately.
• Integration with other tools: Tools that integrate seamlessly with standard container registries, team communication platforms, and continuous monitoring tools foster a cohesive development environment. This allows for efficient version control, streamlined communication, and proactive performance monitoring of test environments.

Modern tools like Coherence provide all of the features above as well as on-demand development environments, which streamline development workflows through cloud-based IDEs. Each environment is pre-configured and is unique to a user and code branch, providing a clean slate for development tasks.

For instance, a developer working on a new feature branch can build a clean, isolated development environment specific to their code branch. This environment includes all the necessary dependencies, configurations, and access to backend resources like databases. The developer can work on their feature without worrying about conflicting changes or local setup complexities. Once finished, they can easily share the environment with other developers for review or merge the changes back into the main codebase.

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Last thoughts

Managing ephemeral test environments offers significant advantages for development teams but can also be a resource-intensive task. Environment-as-a-service tools like Coherence streamline the process of creating and managing test environments. This significantly reduces the internal resources required to build and maintain a test environment. To learn how you can make use of branch preview environments, read the Setting Up Preview guide.