Software quality assurance is facing a growing efficiency crisis. Traditional automation frameworks often collapse under constant maintenance and are plagued by test failures that delay critical releases and disrupt development cycles. These issues translate into millions in business losses when unstable scripts derail launches and engineering teams spend more time fixing broken code than building new features. The urgency for transformation is clear, especially as experts forecast the global AI in software testing market to surge to $10.6 billion by 2033, growing at an annual rate of 18.7 percent.

Answering this critical demand, Priya Yesare, Principal SQA Engineer with over 20 years of cross-industry experience, has pioneered groundbreaking innovations in self-healing automation testing powered by artificial intelligence. Holding a master's degree in computer applications, she has developed a next-generation self-healing automation framework that reduces test maintenance by over 40 percent and slashes regression testing time by more than 40 percent, redefining how global teams approach quality assurance. This framework leverages the synergy of machine learning, data analytics, and modern DevOps practices, providing a blueprint for intelligent and adaptive testing in fast-paced enterprise environments.

Yesare began her career in 2004 as a software developer, building enterprise applications with VB6 and C#.NET. Her transition into quality engineering was marked by major contributions at financial institutions and enterprise platforms, where she helped architect automation systems that support mission-critical operations handling billions in transactions.

Her extensive experience across the insurance and financial services industries has given her a rare vantage point on quality assurance challenges critical to high-stakes, regulated Environments.

A central theme in Yesare's journey is the evolution from static, script-driven automation to intelligent and adaptive systems. She recognized that test failures often originate from minor UI changes, such as altered DOM structures, new element IDs, or dynamic components. In response, she championed using supervised machine learning models such as decision trees and neural networks to predict locator breakage and automate the healing process. These models continuously learn from historical test failures, UI change logs, and runtime context, which improves the framework's ability to resolve issues autonomously. Her self-healing automation approach significantly reduces the need for manual intervention by the test team and enables continuous feedback loops within the CI/CD pipeline. This results in faster releases without sacrificing quality or compliance.

A New Era in Test Automation

Facing persistent challenges with unstable automation scripts and costly manual interventions, Yesare created a custom testing framework using Playwright, TypeScript, Java, Selenium, and Cucumber. Her solution supports complex workflows across service provisioning and multilingual, cross-browser environments, significantly improving reliability and coverage in continuous delivery pipelines.

The framework's integration of machine learning for predictive Healing is revolutionary. The AI layer anticipates UI changes by analyzing historical test run data, tracking DOM mutations, and learning from patterns in element breakage. When a test script encounters a missing or changed locator, the self-healing engine applies computer vision and NLP-based algorithms (such as fuzzy string matching, element similarity scoring, and contextual analysis) to identify the closest matching element or suggest corrections in real-time.

These capabilities are orchestrated via RESTful microservices within the pipeline, enabling seamless plug-and-play integration with CI/CD tools such as Jenkins, Azure DevOps, and GitHub Actions. The system leverages asynchronous event streams and containerized environments (using Docker/Kubernetes where needed) to execute, heal, and report on thousands of tests in parallel, even as applications evolve rapidly.

When unexpected changes occur, the system dynamically adapts, ensuring continuity with minimal human intervention. This results in significantly reduced flakiness, near-zero false positives, and an overall automation coverage increase of more than 60 percent within two release cycles. At the same time, regression testing time is cut nearly in half.

Security and Innovation at Scale

Yesare's innovations extend beyond functional testing. She has seamlessly integrated tools like OWASP ZAP and Microsoft Security Copilot into her self-healing framework, automatically prioritizing security vulnerabilities and enforcing policy-as-code compliance checks within development cycles. Her solutions streamline the CI/CD pipelines by incorporating security validation directly into every build and deployment cycle. Automated workflows trigger dynamic security scans, correlate scan results with code changes, and initiate remediation steps or approval gates based on risk thresholds. These innovations have notably reduced security remediation times by 70 percent and significantly enhanced overall platform resilience, enabling rapid releases without compromising security or regulatory compliance.

Her work has also accelerated the adoption of smart testing systems in healthcare technology, where backend validation, compliance requirements, and complex data processing demand high accuracy. By implementing her methods, engineering teams have reported up to 30 percent increases in efficiency and significantly reduced script maintenance workloads.

Research Leadership and Industry Recognition

Yesare's leadership in AI-driven software testing has garnered recognition across technical and industry circles. Her work in self-healing automation, predictive quality analytics, and intelligent CI/CD systems has been cited by QA professionals and adopted by organizations working to modernize their engineering practices. These contributions have influenced the evolution of automated quality assurance, particularly in highly regulated sectors such as finance and healthcare.

In recognition of her expertise, she has been invited to speak at major international conferences, including the 2025 Peer Engineering Innovation Summit and the 5th International Conference on Intelligent Vision and Computing, where she presented her innovations in AI-based security testing and adaptive automation frameworks.

Shaping the Future of Software Engineering

Yesare's leadership extends beyond individual tools or platforms. She has established the best practices, mentored cross-functional teams, and developed modular testing strategies that reduce defects and enhance reliability throughout the product lifecycle.

Her framework transforms QA from a reactive checkpoint to a proactive, strategic function.

Through predictive analytics, self-healing scripts, and intelligent diagnostics, she has reimagined QA as a data-driven discipline that anticipates problems before they surface.

By enabling developers to focus on innovation while automated systems handle routine validation, her work marks a shift toward smarter, faster, and more resilient engineering processes. As she continues to drive advancements in AI-integrated CI/CD and security automation, Priya Yesare is not just solving today's QA challenges; she is actively shaping tomorrow's industry standards.