Continuous testing is a process that tests applications under development continuously and automatically, throughout the software development lifecycle.
Table of Contents
Continuous Testing Defined
Continuous Testing on the Rise
Continuous Testing Benefits
Continuous Testing and Test Automation
Continuous Testing Challenges
Agile Test Data Management Using Business Entities
Continuous Testing Defined
Continuous testing involves testing that starts very early in the delivery pipeline (i.e., shift-left testing), continues all the way to production. It delivers high-speed, high-frequency cycles, allowing testing teams to check the quality of their work every step of the way, and evaluate the business risk related to the product.
Continuous Testing on the Rise
In today’s complex and busy software development arena, continuous testing builds a streamlined process of development-testing-deployment, enabling teams to discover errors quickly, and move to production much faster. It improves CI/CD pipeline speeds and optimizes the workflow for development, testing, and QA teams.
It’s no wonder that the global continuous testing market is expected to reach a value of $3.45 billion by 2026, at a CAGR of more than 15%. A recent survey examining the views of senior decision-makers at large enterprises showed that 55% have already implemented continuous testing in their organization, and the rest plan to do so. The opportunity to gain feedback throughout the process is a valuable tool for meeting business goals.
Continuous Testing Benefits
The rapid adoption of continuous testing isn’t coincidental. Here are 6 good reasons for enterprises to embrace this approach:
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Business risk evaluation
Risk analysis is one of the leading objectives of continuous testing, as it allows testing teams to assess business risk coverage, at any given time, and address it accordingly. -
Accelerated delivery
Constant feedback shortens the time-to-market cycle, and builds an agile process in a matter of hours. -
Dual testing approaches
Continuous testing essentially combines shift-left testing, which brings frequent, agile testing to play early on, and shift-right testing, which includes testing in a production environment. -
Improved product quality
Continuous testing can improve code quality, through actionable feedback provided across the pipeline. It also boosts the overall customer experience, and protects the final product release. -
Enhanced consistency
This advanced form of testing maintains consistency, by using the same test configuration throughout the process. -
Seamless pipeline integration
Testing is conducted at different stages of the development cycle, including development, pre-release, delivery, deployment, and production. Previously separated teams, responsible for these stages, are merged around the same business needs.
Continuous Testing and Test Automation
There is a strong connection between continuous testing and test data automation. The idea is that when each part of the development cycle is automated, it ensures testing repeatability, and consistency.
Once the test automation suite is generated and an automated test environment is built, test data automation can take care of data masking, or tokenization, and subset provisioning. This approach, using dynamically provisioned testing environments, can further accelerate software delivery by performing multiple testing phases including performance testing, as well as parallel regression and progression testing.
Continuous Testing Challenges
At every step of the way, we need production-grade test data to be available and accurate. This isn’t an easy goal to achieve in complex enterprise IT environments spanning many production systems. When asked about their main continuous testing obstacle, more than half of professionals in the field stated that test data availability prevents them from implementing this method.
We must consider a couple of reasons causing this challenge:
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Speed
Obtaining production-grade test data can take up to 2 weeks, causing delays and testing bottlenecks. That’s because test data is scattered across multiple and complex enterprise IT systems, and that extracting large datasets – without impacting production system performance – takes too much time. -
Anonymization
Masking the data is critical to meet today’s data governance standards and regulations. Testing teams prefer working with production-grade data to ensure coverage. Data masking tools that transform real data into realistic, yet fake, data, must also maintain referential integrity despite the data anonymization process. So, we need to bring together test data from multiple systems, mask it to prevent access to sensitive information, and then ensure that it remains accurate and coherent.
Agile Test Data Management Using Business Entities
The entity-based test data management approach ensures that your test data is integrated, compliant, and instantly obtainable. And when the process is based on business entities, which create a separate data area for each customer, the following benefits are achieved:
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Fresh, up-to-date production-grade data is always available, and any change made to the data can only affect that specific entity. This makes it easy for your test data generation tool to track and update changes.
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It becomes easy to both anonymize data and protect its referential integrity. The business entity structure ensures that the relevant, protected data stays complete and consistent all the time.
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When not enough production-grade data is available, synthetic data generation tools can be employed to create new version fields, clone entities, and more. This allows us to deliver the needed test dataset, on demand.
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When all business entities are managed in a centralized test data warehouse, testers can provision test data using a self-service portal.
The Right Continuous Testing Approach
The right continuous testing approach offers real-world, production-grade anonymized data that maintains full referential integrity. Test data automation enables quick and reliable test data subset provisioning across any parameters required for the specific test scenario, regardless of the data source in question. Only this process will offer sufficient accuracy, speed, and flexibility to form a successful development cycle based on continuous testing.
