Hey everyone, this is Joshua Goum. I have been in the software industry for more than 13 years in development and quality engineering space. Today I'll be talking about the triple threat, which is a technologist reducing the testing costs. Primarily we'll talk about the generative ai, predictive analytics and self-healing frameworks, how transforming their to the testing landscape for forward thinking organizations. The traditional testing limitations we are talking about time and test creation. Manual script consumes valuable engineering resources. Teams struggle with the coverage gaps. When you have a manual testers creating the test scripts, you are going to have the coverage gaps and reactive. Defect detection problems. Discover too late critical issues to reach production environment. When you have a P one issues in production, it's going to, damage the trust of the customer. Also, it's going to impact that top line or bottom line revenues. And the third one is the high maintenance overhead. When you have the automation scripts which breaks with the U changes, our false positives drain the resources and trust. So we, the solution for all this problems is our solution. What we're talking about is the A testing triangle with the generative AI and predictive analytics and self-healing frameworks. The first one is a gender dua, where you can create a comprehensive test script autonomously identifying the edge cases. Human testers often miss, so when you, obviously, when you create the gender dua, the test scripts, it's going to have a, better coverage than the manual test. Test case script creation. The predictive analytics leverage historical data to focus testing resources on high risk areas with a greater potential impact. What we are talking about is whatever areas having, when gather the historical data and see where you have higher P one bucks, which is causing the revenue, and you can deploy your resources there. The third one is the self-healing frameworks, right? Automatically adapt to UA changes, eliminates the false positives and maintenance and test integrity without manual intervention. When you have all these three, you will have the, if you if you go through the numbers right, by reducing the manual test creation by up to 70%, cutting maintenance cost by 65% and accelerating release cycle by 40%, instead of looking at a. Testing as a bottleneck, then you are looking at it as a competitive advantage. The results is the high quality release, faster time to market, and dramatically lowering the testing costs across your entire development pipeline. So the first one is generative ia, right? The before generative IA before when you have a time consuming manual script creation requiring specialized expertise coverage gaps, leaving critical scenarios untested and excessive developer hours devoted from the feature development variable quality dependent on the individual. Tester skill levels right after the generative wave, we are talking about a power skip generation in minutes instead of days. Comprehensive coverage, including edge cases, human affirm needs and development resource allocated to high value innovation and consistent enterprise grade quality across all test suits, not just one area where you have the experts. The second one is a predictive analytics. What is what it is like an anticipating the issues, right? Risk identification, right? High risk models flagged before deployment. Using the historical data, you can identify the what is the high risk areas. And focus testing their resources directed to vulnerable areas and, defect prevention, critical issues caught before production, a score patterns, and complex metrics. And you can follow this through this cycle. Over a period of time you are going to refine and get a better results. The third one we are talking about is self feeling frameworks, right? Automated maintenance where U UA changes, breaks, existing tests, right? A demands, failure patterns, and test scripts automatically updated. Fix verified without human intervention. There is multiple tools available as of today in the market. You can see what works best for your needs. You are talking about the real world results by industry, right? You can see across healthcare or FinTech or e-commerce, right? Test case. Test case creation time is reduced by almost like 70%. Defect detection is increased by 40%, and maintenance hours decreased by almost 80%. That this is a grade. We are talking about time savings of 74% and 63% of cost reduction, and 40, 41% of defect prevention and 3.5 x of coverage increment. Now how it can be implemented, right? You are talking about architectural integration patterns, right? First standalone implementation, right? Begin with the isolated tools. Test E each AI component independently before integration. Then you can do the portion integration. Connect to components, build confidence in the approach with the, in a limited scope, full ecosystem integration, right? Implement complete feedback loop. Allow all components to share data and insights. Obviously you cannot a hundred percent rely on the ai. You need to have ethical AI governance framework where you have a transparency, light clear documentation of AI decisions, explainable test generation, logic editable, prediction, rational and accuracy, right? Regular validation against human experts, continuous model retraining. Confidence scoring for predictions. The third one is a human oversight the finalization authority with the QA leads. Regular review of AA performance over rate capability for all automated actions. Next one is a tool chain integration approach, right? Where you have the a PA integration in the first place. We are connecting through standard rest interfaces. Then you have the pipeline embeddings with integrating into CACD workflows. Then you have the data sharing where you are gathering all the data, extra centralized matrix repository, like elastic search or some kind of repository, UN unified dashboards, right? Create a comprehensive visualization layer, right? It can be Grafana, Splunk, or something else, right? The implementation roadmap, what we are talking about, the phase one is a foundation, right? Selecting tools. Obviously you have hundreds of tools or maybe thousands of tools available in the market. You have to see what is the right fit for your needs and your enterprise. Establish the baseline metrics. Try shell team members in shell deployment, right? Implement individual components, validate results against control groups and integration, right? Connect component feedback loops. Monitor system performance, then you can expand scale across teams, refine process, document best practices. When you do these things right, the key takeaways where we are talking about is the AI synergy, right? The combination of technologies create greater impact than individual tools, right? Like obviously each EA tool will bring what we bring to the table is going to have a different result. But when you combine all different technologies are different tools, you are going to have a greater impact and proven results, right? Real world implementation demonstrates significant return investment across industries. When these three technologies. Together implemented. We already seen so many, enterprise level results. Enterprise level companies giving, getting the better results proven results with this. And practical pathways. Smart small with a modular implementation before full integration and human partnership, right? A enhances human testers rather than replacing them. With that, I'm done. Thank you very much.