How Does AI-Driven Test Orchestration Help Product Teams Achieve Continuous Delivery Excellence?

How Does AI-Driven Test Orchestration Help Product Teams Achieve Continuous Delivery Excellence? – Insights From LambdaTest

Mudit Singh

August 7, 2024

The author of this article is Mudit Singh, Head of Growth and Marketing at LambdaTest

Although continuous delivery promises rapid software delivery cycles, still ensuring quality at pace poses a significant challenge. Traditional testing approaches often lag, and they are unable to keep up with rapid deployment cycles and the increasing complexity of modern software. Further, the traditional test automation tools, though valuable, often struggle to adapt to frequent code changes and lack the intelligence to prioritize these tests effectively.

On the other hand, AI-driven test orchestration goes beyond automation and leverages AI and ML algorithms to create a smarter and more efficient CD process.

Need for Smart Test Orchestration Process

Test Script Fragility and Maintenance Issues:

Frequent code changes lead to fragile test scripts needing constant updates and creating significant maintenance burdens. In such a scenario, adding new features and refactoring code adds to more complexity making it tough to maintain comprehensive test coverage. This can further lead to the accumulation of technical debt as complex and poorly designed test scripts can slow down the process.

Sluggish and Inefficient Test Execution:

For larger test suites linear test execution is insufficient resulting in a long and slower test execution process. Another factor is manual test prioritization is often time-consuming, causing critical tests to be overlooked and delaying developer feedback.

Dealing with Flaky Tests:

Flaky tests create confusion, often disrupting the build process. These unreliable tests are difficult to debug, eroding the trust in the testing process and allowing the bugs to slip through to production. This also delays releases or forces the deployment of untested code, undermining the goal of continuous delivery and reliable software delivery.

What can AI-Driven Test Orchestration Offer?

Some of the ways in which AI-based test orchestration can solve the challenges are:

1. Improved Test Coverage with Intelligent Test Selection:

AI examines the code changes and application behavior to uncover which areas are most important for testing to accomplish dynamic testing. This helps guarantee that within each build, all vital functions are tested despite the frequent code modifications, which leads to better product quality and effectiveness of tests.

2. Increased Efficiency Due to Automated Execution and Monitoring:

AI can also assist with the planning and managing of tests by scheduling, running, and evaluating tests in different environments and on different platforms. This makes it easier for human testers to spend more time in areas such as the test strategy or the analysis of failures that are more challenging to diagnose.

3. Reduced Costs and Enhanced Reliability with Self-Healing Tests:

AI takes notice of previous failed tests and adapts test scripts to the change of code automatically. Some algorithms are designed to detect and classify flaky tests and provide solutions for their correction as well as minimize the amount of time needed to maintain flaky tests. This helps to reduce unnecessary test script maintenance so that the tests are functional, reduces cost, and improves reliability due to the elimination of flaky tests.

4. Improved Decision-Making with Data Collection and Analysis:

AI driven test orchestration can bring more value through analyzing large volumes of test data to gain better insights about the quality of software. This assists developers and testers in making informed decisions about the optimization of test strategies, testing resources, and the areas of the product that might require future development.

Getting Started with AI-Driven Test Orchestration for Your Organization

Implementing AI-driven test orchestration becomes easy with a Just-in-time-orchestration (JITO) platform which speeds up your entire test execution process allowing your team to focus on impactful tasks.

Self-Assessment for Implementation:

Before implementation, conduct a thorough assessment of your testing landscape. Organize by starting with an assessment of existing tools, measurement of test coverage, and benchmarking of the team. This evaluation will allow focusing on where the test orchestration could be most helpful such as reducing your test execution time or your test case prioritization process.

Choosing the Right Tools based on Your Needs:

Selecting an efficient JITO partner is critical for successful implementation. Look out for tools that offer seamless integration with your current tools and CI/CD pipeline. Evaluate the tools based on the capabilities they offer, for instance, self-healing tests, test intelligence as well the user-friendliness of the platform. Here you can leverage the frail trials and demos that many tools today offer.

Start Small and Scale Gradually:

Start the implementation with a small pilot project, prioritizing the tests that have the most significant impact on the functionality or user experience. Once your team starts using the platform, continuously monitor its performance and refine your test selection and configuration based on real-time results. This approach ensures that you adapt to changing application dynamics and test requirements effectively.

Build the Expertise and Foster Collaboration

One ongoing step after the implementation process requires your team to build expertise. Invest in training sessions or conduct regular KTs to familiarize the QA team with the capabilities of test orchestration and encourage collaboration between the Dev and the QA team. This will lead to a smoother implementation process and will ensure that everyone is aligned on the objectives.

The Road Ahead

AI- leveraged test orchestration is not only a technological advancement but a transformative approach that can help you redefine your testing strategy. By integrating test orchestration, you invest in the future of your development processes, enhancing both speed and quality.

The benefits stand out clearly- experience accelerated delivery cycles with AI-powered test optimization, ensuring that your teams spend more time innovating rather than testing. With capabilities like self-healing tests and intelligent test prioritization, AI-driven test orchestration promises your team faster and more reliable software releases.

Additionally, the reduction in script maintenance leads to significant cost savings, streamlining your testing workflows. Last but not least, it is also capable of deep-diving into test results, providing insights into code health, performance bottlenecks, and potential test failures.

Talking about its future advancements, AI-driven test orchestration can become smarter. The more data it consumes, the smarter it becomes. Over time, AI can learn to optimize test execution further, reducing overall time and effort.

About the Author

Mudit Singh is product and growth expert with 15+ years of experience building great software products. A part of LambdaTest’s founding team, Mudit Singh has been deep diving into software testing processes working to bring all testing ecosystems to the cloud. Mudit currently leads marketing and growth for LambdaTest as Head of Marketing & Growth. LambdaTest is a leading continuous quality testing cloud platform, headquartered in San Francisco, US. LambdaTest has 2mn+ users and 10,000+ customers across the globe.