Dynamic, adaptive automation is delivering a more accurate and efficient era for software testing. With adjustments to ensure data, environment, and expertise are ready, businesses are seeing tremendous benefits from the shift to proactive AI-powered testing.
AI advancements are reshaping so many business processes, and software quality engineering and assurance (QE&A) is proving a ripe opportunity for Agentic AI-powered evolution. Once a deeply manual process of identifying and resolving bugs, QE&A has seen a steady shift toward automation but still demands a great deal of labour-intensive, time-constrained interventions. Humans in the loop are essential in critical systems, but AI’s arrival greatly expands coverage, accuracy, and efficiency in finding issues for human experts to resolve.
Our customers are already saving hundreds of engineering hours per sprint and improving release cycles by as much as 40%. Automation is reducing time to market, giving their engineering teams clarity on where to apply their expertise to the work at hand every step of the way.
How is AI changing QE&A?
AI is revolutionising many domains, with AI-powered agents now set to perform tasks that were traditionally handled by humans. From test case generation to defect detection and analysis, machine learning algorithms and intelligent automation are delivering powerful benefits to the testing lifecycle. Let’s take a closer look.
Enhanced test coverage: Traditional testing methods often struggle to cover all possible scenarios and edge cases. This can lead to potential vulnerabilities and undetected bugs. With AI-powered tools, both generative and agentic, teams can analyse vast amounts of data, identifying more patterns and generating comprehensive test cases that cover the widest possible range of scenarios, ensuring more robust and thorough testing.
Improved accuracy and efficiency: By reducing human error and automating repetitive tasks, accuracy and efficiency are greatly enhanced. Machine learning algorithms can quickly analyse code, detect anomalies, and predict potential defects, allowing testers to focus on more complex and critical aspects of the application. This not only speeds up the testing process but also reduces the likelihood of overlooking critical issues.
Dynamic, adaptive testing: Agentic AI-driven test automation goes far beyond traditional scripting, unlocking more dynamic and adaptive test processes. These AI algorithms can respond to real-time data, adjusting test cases to generate and execute test scripts that meet the demands of an evolving software environment. This adaptability ensures tests remain relevant and effective through frequent software updates and changes.
Automated defect detection: AI can harness the power of predictive analytics to anticipate potential issues before they arise. By analysing historical data and identifying trends, AI models can predict areas where the software is likely to encounter problems. QA teams can act on these insights to proactively address issues, significantly reducing the time and resources spent on fixing defects after they have been discovered.
Continuous testing in CI/CD pipelines: In an era of continuous integration and continuous delivery (CI/CD), AI-driven testing can seamlessly integrate into the development pipeline. Agentic AI-powered tools can monitor code changes, trigger tests, and provide real-time feedback to developers. Continuous automated testing ensures defects are identified and resolved early in the development cycle, leading to faster release cycles and higher-quality software.
How ready is your testing process for AI?
The value of AI-powered software testing is clear. But what does it take to prepare systems and processes to integrate these tools? Data, existing tools, and team skill development need to be assessed ahead of taking the leap.
AI relies on high-quality data to deliver its benefits. Poor data can lead to inaccurate and biased outcomes, while poor data management can result in breaches of ethics responsibilities. Ensuring data is accurate, relevant, and representative of real-world scenarios is crucial. This may require improvements in data collection, cleansing, and management practices to ensure AI-driven testing is as effective as possible.
Existing tools and processes must also be examined carefully to understand what customisations and adaptations may be required to integrate new AI-powered testing tools into current frameworks, CI/CD pipelines, and development environments.
Finally, QA teams need to be trained to understand how the AI technologies work and how they must work with these tools to take advantage of the new AI-powered processes and insights. Understanding the fundamentals of what machine learning and data science are doing to enable these automations can be of great assistance, but most important is assessing where existing testers require additional training and where new talent with specialised skills is required.
Once alignment is achieved across these areas, AI can deliver rewards to your QE&A team that accelerate their efforts far beyond traditional capabilities.
AI testing in practice
Cognizant is already seeing significant, measurable improvements in testing efficiency through the tools we are rolling out for our clients in Australia.
One major telecommunications company was finding lengthy regression testing cycles was impacting sprint efficiency and overall time to market. We deployed an AI Test Optimizer with semantic understanding it was able to consolidate test cases based on contextual similarity scores. This significantly reduced the time and effort required for test case management. The result was a 72% optimization in regression test cases and a drop-in regression execution cycle time from five days to just two.
In a proof of concept with a major financial institution, Cognizant has implemented an AI-powered quality engineering solution to automatically convert user stories into test automation scripts. It generates testing artifacts from business documentation, including PDFs, JIRA tickets, and architecture diagrams. Preliminary analysis suggests the AI-driven approach could reduce test creation time by 40%, saving hundreds of engineering hours per sprint. It could also accelerate release cycles by 30-40% through faster test generation and execution.
In a phase two trial with the same institution we will explore agentic capabilities, exploring persona-based workflows such as creating distinct Business Analyst and QA Engineer agents that can interact through conversation-based workflows. This may facility more contextually aware and meaningful outputs through a simulation of more natural collaboration in the software development lifecycle.
Cognizant solutions for the AI testing era
Cognizant is leveraging Agentic AI in various aspects of Quality Engineering (QE).
Cognizant has developed a comprehensive AI Testing Framework that strategically aligns AI with software testing objectives. This framework includes identifying areas for improvement, evaluating current testing practices, and setting specific goals. By implementing robust data management policies, ensuring data quality, privacy, and security, and staying informed about evolving regulations, Cognizant ensures compliance and continuously monitors and evaluates Gen AI performance. This approach has made testing processes more efficient and effective.
Additionally, the Cognizant Neuro® AI Engineering platform enables organizations to build resilient AI solutions that are customised for their data sets, leading with Agentic and Generative AI. This platform supports a wide range of use cases, including customer-facing applications and operational improvements. It is designed to reduce the complexity and operating costs of enterprise infrastructure and technology, using AI-powered tools to make modern IT tasks run smoothly and efficiently. By providing an end-to-end, single pane view of observability, AI, workflow, and automation tools, Cognizant Neuro® IT Operations improves resilience, conquers complexity, and gives full visibility and control over IT operations, including AI agents allowing automated resolution of issues in both Production and pre-Production environments.
Looking ahead, we are already deploying Agentic AI capabilities with clients to advance their Quality Engineering processes. Distinct Business Analyst and QA Engineer agents can interact through conversation-based flows, simulating natural collaboration in software development lifecycles to further enhance the quality and relevance of generated test artifacts.
It’s an exciting time as we watch clients close in on 100% automation coverage, save millions in efficiency gains, and reduce cycle times from weeks to days, and ultimately minutes. A new era is possible and it’s here today. Are you with us?
