The Impact of Continuous Testing- How Organizations Transform Their Testing from Reactive to Innovative

Continuous testing is now the preferred way for teams to create, test, and release high-quality apps. If you’re involved in app development, you know how crucial continuous testing is to not only meet but exceed expectations.

However, not all continuous testing strategies are the same. The approach you take may change based on your team’s experience with continuous testing and where your app is in development. This is where continuous testing maturity models help. They show how to improve testing strategies at different stages, moving from a basic approach to a more innovative one, resulting in better applications that exceed expectations.

What is Testing maturity?

Testing maturity refers to how well testing processes are developed, managed, and optimized. It is an indicator of the effectiveness of the testing process, contributing to app quality. As the testing process matures, the team improves testing efficiency, results, and innovation.

Characteristics of Maturity level

5 Pillars of a Continuous Testing Maturity Model

Think of your testing maturity model in two phases: Capability and Assessment. Here, we’ll focus on the Capability phase and its five key parts (or pillars), which vary based on your team’s current capability level.

• Initial: This is the entry-level pillar, ensuring the software runs, but it lacks proper testing tools and resources. No formal testing process exists, and decisions are made with minimal checks.
• Defined: At this stage, debugging goals are established, and testing is done with purpose. Testing happens after coding to check if the software meets basic requirements.  This phase is also known as the basic phase.
• Integrated: Testing is now part of the software development process. It’s more organized, with testing objectives prioritized. Testing is now considered an essential aspect of software development. This phase is also known as the automated phase.
• Management & Measurement: This pillar involves measuring the success of tests. Bugs are tracked, categorized, and documented based on their severity. Reviews at each stage of the development process are treated as tests. Regression testing becomes incorporated to collect and record in a database for reference. Bugs are identified, logged, and given security levels based on severity.  This phase is also known as the orchestrated phase.
• Optimization: Now is the time to determine how well your testing process is managed and defined. At this stage, the team tracks both test effectiveness and costs. Tools are available to support advanced testing metrics and defect tracking.  This phase is also known as the intelligent phase.

Functional Testing

Functional testing is the process of validating the functionality of a software application. It assesses whether the primary functions of a software application that is working align with the overarching goals of the app. Functional tests result in pass/fail scenarios because the features either work as designed or not.  Some examples of functional testing types include Unit Testing, Integration Testing, Sanity Testing, and Smoke Testing.

Functional Testing Maturity Model

Take automation, for instance. In Phase 1, only around 10 percent of the example is automated. While in Phase 5, at least 80 percent is automated. In the initial phase, teams are only testing new features and end of sprint only, while they can test a full regression on every pull request by Phase 5.

Load Testing

Load Testing evaluates how well an application performs under real-world conditions, such as high traffic during events like a Black Friday Sale. Its goal is to ensure that the application can handle heavy usage and to identify potential weak points in advance. The Load Testing Maturity Model is divided into phases:

Phase 1: Ad-Hoc Load Testing

• Focus: Testing is unstructured, often done without a clear plan, leading to unpredictable results.
• Impact: Production defects frequently arise, leading to problems for end users. The focus is mainly on fixing issues after they occur (damage control).

Phase 2: Fully Optimized Load Testing

• Focus:  Load testing is fully optimized and advanced. The team explores the limits of the application with techniques like chaos testing and UX testing.
• Impact: The testing process ensures top-tier application performance, even under extreme conditions. At this stage, the business achieves a high return on investment (ROI) by delivering best-in-class applications.

Read More: Advance Use Case of Load Testing Using JMeter

Impact of Continuous Testing Maturity Models

Now that we have delved into the details of functional and load-testing maturity models, we will take a closer look at the impact continuous testing maturity models can have on different areas of business, starting with the business impact itself.

BUSINESS IMPACT & METRICS SUMMARY

• Business Impact Initial Phase: In the initial phase, the quality of the product is unpredictable, and the costs are high. Most testing is done manually, with very little automation. Instead of involving the entire team in ensuring quality throughout the development process, only a few individuals or experts are responsible for it. This approach is less efficient, leading to poor and inconsistent quality. Testing coverage and data tracking are often done without a proper system, resulting in waste and higher costs.
• Business Impact – Basic Phase: In the basic phase, costs are reduced, and code quality improves compared to the initial phase. There is also better test coverage, faster setup of test environments, and more effort put into designing and maintaining tests. However, some defects may still slip through to production, and certain testing requirements may still be unclear.
• Business Impact – Automated Phase: In the automated phase, both development and testing quality improve, and the overall app quality increases. Testing is moved earlier in the development process (“shift left”), automation is used much more, and tests run faster.
• Business Impact – Orchestrated Phase: In this phase, the speed and flexibility of application releases greatly improve. Almost all testing is automated, execution times are much shorter, and continuous testing is fully integrated into the DevOps process. Leaked defects are very rare.
• Business Impact – Intelligent Phase: This phase is focused on optimization and predictability. Defects are prevented before they happen, and testing is well-planned and maintained. Tests are prioritized effectively, and businesses can rely on the process without worrying about quality issues. All tests are fully automated, and resources are well-managed, ensuring the best possible quality for the applications and better business outcomes.

Process impact and metric summary

PROCESS IMPACT AND METRICS SUMMARY

• Process Impact– Initial Phase: Testing is mostly manual, including test design, execution, and environment setup.
• Process Impact – Basic Phase: Test design and unit testing are automated, with some API automation.
• Process Impact – Automated Phase: API automation increases and testing is tied to CI cycles.
• Process Impact – Orchestrated Phase: Test automation reaches 60%, with a focus on CD integration.
• Process Impact – Intelligent Phase: Predictive testing becomes the norm, with self-healing tests and machine learning optimizing the process.

Technology Impact Across Continuous Testing Maturity Phases

When looking at the continuous testing maturity model through a technology-based lens, the Initial phase is often defined by the use of ad-hoc tools with some (but minimal) integration. Spreadsheets are used for test design release tracking, and mocking/ stubbing tools.

TECHNOLOGY IMPACT AND METRICS SUMMARY

Technology Impact Across Continuous Testing Maturity Phases

When looking at the continuous testing maturity model through a technology-based lens, the Initial phase is often defined by the use of ad-hoc tools with some integration. Spreadsheets are used for test design release tracking, and mocking/ stubbing tools.

Technology Impact – Initial phase
In this phase, technology use is minimal and tools are used on an ad-hoc basis. There is little integration between systems. Spreadsheets are used to design tests and track releases, while basic mocking and stubbing tools are implemented for testing purposes.

Technology Impact – Basic Phase
In this phase, teams begin using more integrated tools for testing. There is a basic test platform in place, unit testing tools are used, and some automation tools are incorporated. Additionally, basic virtual services, API testing, and Continuous Integration (CI) tools are implemented, along with app release tracking.

Technology Impact – Automated Phase
The Automated phase sees greater integration, particularly between the test platform and the CI platform. Test-driven development (TDD) tools like Gherkin and Cucumber are introduced alongside automation tools like Selenium. This phase also includes advanced virtual services, API monitoring tools, and methods for subsetting and masking test data.

Technology Impact – Orchestrated Phase
In this phase, the test platform becomes fully integrated with the Continuous Delivery (CD) platform. Tools for A/B testing are used, and systems are in place for tracking artifacts through the testing and release process.

Technology Impact – Intelligent Phase
In the final stage, the test platform is integrated with analytics and machine learning platforms. Continuous Delivery/Continuous Deployment (CD/CDI) tools are used to aggregate data across the lifecycle.  Predictive analytics and machine learning frameworks optimize testing, and there is a centralized automated system for managing test data and analysis.

Conclusion

By following a continuous testing maturity model, teams can systematically improve their testing approach, advancing from basic to highly innovative methods that ensure better app quality, reduce costs, and drive business success.