Hi everyone. Welcome to my talk on a powered integration testing, transforming enterprise Reliability through adaptive automation. As modern organizations increasingly rely on seamless integration between diverse applications and systems, conventional testing methods struggle with maintenance burdens, limited test coverage, and inefficient execution. AI driven test automation is emerging as a game changing solution to these challenges. This presentation explores how artificial intelligence introduces intelligence and adaptability throughout the testing lifecycle, from test design to execution and analysis, transforming how we verify enterprise integrations and delivering significant improvements in efficiency, quality, and cost. Before I move further, let me introduce myself. I'm Shiva Yamani, currently working as quality engineering manager with Infosys. I have close to 18 years of work experience in software industry with focus and expertise on software development and quality engineering. During my tenure, I have worked with multiple leading healthcare, banking, insurance and retail organizations across US uk. Providing testing and automation solutions. Before we deep dive into AI powered testing approaches and solutions. First, let's understand what software testing is and what challenges organizations are facing in today's world using traditional test and automation practices. Software testing is a critical phase in software development lifecycle, ensuring that products meet necessary functional. Performance and quality benchmarks before release. Despite advancements in automation, traditional methods of generating and validating test cases still face significant challenges, including prolonged timelines, human error, incomplete test coverage, and high cost of manual intervention. These limitations often lead to delayed product launches and unidentified defects that compromise. Software quality and user satisfaction. In this slide, I'll outline some of the key challenges organizations are facing today to start with growing complexity. Modern enterprise ecosystems create a complex web of connections that traditional testing methods based on static scripts and predefined scenarios struggle to maintain. The second key challenge is maintenance burden. Test maintenance accounts for approximately 30% of total testing effort, creating a significant drag on testing efficiency as systems evolve. And the third challenge is limited coverage, where traditional approaches typically achieve only 40 to 60% test coverage. Missing critical integration scenarios as interactions between systems create exponentially more test scenarios. In this slide, we can notice how organizations are rapidly moving from usage of traditional testing methods to a power testing solutions. The global A enabled testing market is experiencing explosive growth projected to expand from 1.65 billion UD in 2023 to 10.29 billion UHD by 2030, exhibiting a CAG of close to 30%. This substantial growth reflects organization's recognition of a's ability to overcome traditional testing limitations. North America currently dominates with 38.2% market share, followed by Europe at 29.7 percentage, reflecting the mature technology sectors in these regions that typically deal with complex integration challenges. Now, since we talked about. Challenges using traditional testing methods and how organizations are moving towards usage of a powered testing. I will take you through the solutions which can address these challenges using a powered testing approaches. First, a powered test solution is intelligent test case generation, which uses. A, to automatically create comprehensive test cases for software enhancing efficiency, coverage and accuracy. Compared to traditional methods, AI can analyze code requirements and user stories to identify potential weakness and generate test scenarios that cover various use cases and scenarios. This approach helps reduce manual effort, improve test quality, and ensures more reliable software. Some of the key aspects of intelligent test case generation are automated test design. A algorithms analyze application, data flow and architecture to identify critical parts without human intervention, creating comprehensive test scenarios that would be difficult or impossible to design manually. The second key aspect is improved coverage. A driven approaches typically identify 40 to 60%. More edge cases compared to manual test design, significantly improving overall test coverage and risk detection. And the third key aspect is usage of natural language processing. NLP based approaches transform how test cases are created from business requirements, enabling non-technical stakeholders to contribute more effectively through the I interfaces. The next, a power testing solution is usage of self-healing testing frameworks. A self-healing test framework is a type of test automation framework that can automatically identify and fix issues that arise during test execution due to changes in the applications, UI or underlying code. This is achieved by using a driven algorithms to dynamically update test scripts. Ensuring uninterrupted execution and reducing the need for manual intervention. Some of the key aspects of self-healing test frameworks are detecting changes where automatically identify modifications in the application interface, data structures, and business logics. Another self-healing test framework is to analyze the impact, which determines how change affect existing test scripts. And execution path. And the third capability is adaptive tests, which automatically update test scripts to accommodate changes without manual intervention. Self-healing frameworks dramatically reduce maintenance requirements, which typically account for 70% of the total cost of test automation in traditional environments. Research shows that these frameworks can reduce maintenance requirements by up to 60%. With visual, A technologies particularly effective for maintaining UI tests across changing interfaces. The next a power testing solution is usage of autonomous execution and monitoring techniques, which refers to systems that can operate and manage themselves, performing tasks and responding to issues without constant human intervention. This includes self-diagnosis, remediation. And even re-planning based on a real-time data and predefined rules. Essentially, it's the ability of a system to autonomously manage its own operations and ensure its continued performance. Some of the key aspects of autonomous test execution and monitoring techniques are smart scheduling, which determine optimal test execution order based on dependencies and risks. The second aspect is intelligent paralyzation, which distributes test across available infrastructure to ma minimize execution time. And the third aspect is predictive monitoring, which identifies subtile patterns in system behavior that might indicate potential issues. Intelligent test case orchestration can reduce overall testing time by 30 to 50% while maintaining or improving test coverage. Machine learning algorithms can identify subtile patterns in system behavior that might indicate potential issues before they manifest as failures, reducing production incidents by up to 45%. Next, a power testing solution is usage of advanced AI powered anomaly detection and root cause analysis techniques, which leverage machine learning algorithms to identify unusual patterns. And deviations from normal behavior in large data sets. This helps to pinpoint the underlying cause of problems enabling foster and more accurate troubleshooting. Some of the key aspects of advanced anomaly detection and root cause analysis are detecting anomalies which identifies unexpected behaviors across integrated systems. Recommend solutions which suggest. Specific fixes based on historical data, unknown patterns. The third aspect is analyzing the patterns which correlate data across multiple dimensions to find relationships. And the fourth important aspect is diagnosing root causes, which pinpoints underlying issues spanning multiple systems. AI driven root cause analysis can reduce debugging time by 40 to 60%. Compared to manual approaches, visual AI can detect functional and visual anomalies with 99.9% accuracy, significantly outperforming traditional accession based testing approaches. Next AI power testing solution is usage of coverage optimization and gap analysis techniques. Coverage optimization and gap analysis using AI involves using artificial intelligence to identify and address discrepancies between what is currently being covered and what should be covered, whether in areas like software testing, content marketing, or skills within a workforce. AI helps by analyzing data, identifying patterns, and suggesting corrective actions to bridge those gaps. Some of the key aspects of. Coverage optimization and gap analysis techniques are risk-based prioritization. Focus at testing on high risk areas first, then gap identification, discover, untested or under tested parts. And the third aspect is coverage optimization, which maximize coverage with minimal test cases. A driven coverage analysis typically identifies 30 to 40% more coverage gaps compared to manual analysis methods. Machine learning approaches can analyze both code structure and runtime behavior to identify untested or under tested paths. Enabling more comprehensive test coverage. Visual A approaches can achieve 98% test coverage with 99.9% accuracy while. Requiring 80% fewer test cases compared to traditional basic testing approaches, focusing on actual user facing functionality rather than implementation details. In this slide, we'll talk about real world impact and benefits that can be achieved through implementation of a power testing solutions. Using a power testing techniques, 40 to 60% of maintenance reduction can be achieved. Which will decrease in test maintenance efforts. Also, this will help with foster deployment cycles by 30 to 50%. In addition, 25 to 45% of better detection defect detection can be achieved overall, allowing organizations with 35 to 55% cost savings. In this slide, we could see how industries or domains starting from. Banking and finance, healthcare, manufacturing, and retail have started implementing a powered testing methodologies to overcome industry specific challenges. Different industries are adopting a driven testing at various rates. With banking and financial sector leading adoption with a significant market share of 22.1% in the global A enabled testing market in 2022, likely due to the high regulated nature of financial services and the critical importance of maintaining reliable system integrations. The future of AI driven testing promises a more proactive. Efficient and intelligent approach to ensuring software quality AI will automate tasks, improve test case generation, enhance self killing capabilities, and even predict potential defects before they occur. This will lead to foster testing cycles, increase the collaboration and ultimately higher quality software. Some of the key aspects are. Creation of digital twins, where virtual replicas of physical systems are moving beyond product development into quality engineering, enabling organizations to test integrated systems in environments that closely mirror production without the associated risks Adoption of explainable A where emerging capabilities. With AI technologies or addressing transparency limitations by providing insights into AI station making process, thereby enabling testing systems to communicate the rational behind test case selection, defect predictions, and root cause analysis. Third one is through implementation of natural language interfaces, where 62% of quality leaders believe. Natural language interfaces will significantly impact testing practices within the next two years by enabling non-technical stakeholders to directly contribute to test definition without specialized in knowledge. And the last key point is usage of federative learning. Where, which enhances collaborative a approaches enable organizations to share testing insights and. Model improvements without exposing sensitive data, particularly valuable for integration testing across supply chains and partner ecosystems. Conclusion, we could notice that organizations are leaning towards aid driven testing solutions, which will enable them to have competitive advantage in foster turnaround to markets with enhanced quality. Some of the key points are. Measurable business impact. AI driven testing delivers substantial benefits across multiple dimensions, including reduced maintenance burden, accelerated delivery cycles, improved defect detection, fewer production issues, and lower overall testing costs. The second aspect is it's an essential capability as. Enterprises continue to expand their integration ecosystems. Yay. Driven trusting will transition from a competitive advantage to an essential capability for maintaining quality and reliability in increasing complex digital environments. The third key aspect is competitive advantage. Organizations that embrace these advanced testing approaches will be better potent. To deliver seamless integration experiences while accelerating innovation and maintaining their competitive edge in the digital market space. Thank you. Hope This presentation gave an overview of challenges in traditional test and automation methods on the solutions to overcome these challenges by implementing a powered testing methodologies. Thank you again for the opportunity.