BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260316T035853Z UID:5C62AC57-77FC-4933-9032-E60E572117CE DTSTART;TZID=America/Los_Angeles:20260331T190000 DTEND;TZID=America/Los_Angeles:20260331T200000 DESCRIPTION:The San Francisco Bay Area chapter of the IEEE Computer Society invites to our free and open Virtual Tech Talks (no IEEE membership requi red):\n\nEventpage: https://www.eventbrite.com/e/transforming-enterprise-q uality-engineering-practices-tickets-1985250279026?aff=oddtdtcreator\n\nSp eaker: Jyotheeswara Reddy Gottam ([Connect on LinkedIn](https://www.google .com/url?q=https://www.linkedin.com/in/gjreddy/&sa=D&source=calendar&ust=1 774063921225418&usg=AOvVaw3p1OTEbWpnyMYwpkfkEjs-))\n\nTitle: The Triple Th reat: Transforming enterprise quality engineering practices with Generativ e AI\, Predictive Analytics\, and Self-Healing Automation\n\nAbstract: Mod ern software teams face mounting pressure to release high-quality applicat ions faster while managing increasing system complexity and continuous del ivery expectations within modern CI/CD pipelines. Traditional testing appr oaches often struggle to keep pace with rapid code changes\, expanding reg ression suites\, and the rising cost of maintaining automation frameworks. These challenges frequently lead to delayed releases\, increased testing costs\, and defects escaping into production.\n\nThis presentation explore s how the “Triple Threat” of AI-driven testing technologies—generati ve AI for test script creation\, machine learning–based predictive defec t analytics\, and self-healing automation frameworks—is transforming ent erprise quality engineering practices.\n\nFirst\, generative AI accelerate s test development by automatically generating test cases\, scripts\, and data from requirements\, user stories\, or code changes\, significantly re ducing manual scripting effort. Second\, predictive defect analytics power ed by machine learning analyzes historical defect patterns\, code churn\, and previous test outcomes to identify high-risk components and prioritize testing efforts where failures are most likely to occur. Third\, self-hea ling automation frameworks intelligently adapt to UI or API changes\, mini mizing brittle test failures and reducing the costly maintenance typically associated with large automated test suites.\n\nWhen deployed together\, these technologies reinforce one another: generative AI expands test cover age\, predictive analytics focuses testing on the most critical risk areas \, and self-healing automation ensures test suites remain resilient despit e frequent application updates. Applied across API testing\, functional te sting\, integration testing\, and end-to-end testing\, this integrated app roach enables organizations to modernize their testing strategy while impr oving reliability.\n\nThe combined impact allows enterprises to reduce tes ting costs by up to 40%\, accelerate release cycles by 30%\, and improve d efect detection rates by over 50%\, demonstrating how AI-driven testing ca n deliver measurable improvements in both quality and delivery speed acros s enterprise software systems.\n\nBio: Jyotheeswara Reddy Gottam is a Soft ware Engineering Leader with over a decade of experience in the retail and e-commerce industry. Currently a Senior Software Engineer at Walmart Glob al Tech\, he leads end-to-end testing strategies for high-traffic marketpl ace platforms\, driving scalability\, reliability\, and performance for sy stems handling millions of daily transactions. He specializes in Gen AI\, ML\, AI agents\, RAG\, test automation\, performance engineering\, and CI/ CD enablement. Throughout his career\, he has architected scalable automat ion frameworks\, reduced regression cycles significantly\, improved releas e velocity\, and ensured platform stability during peak traffic events. He has also led cross-functional initiatives across payments\, inventory\, p ersonalization\, and mobile platforms.\n\nSpeaker(s): Jyotheeswara Reddy G ottam\n\nVirtual: https://events.vtools.ieee.org/m/548913 LOCATION:Virtual: https://events.vtools.ieee.org/m/548913 ORGANIZER:ruben.glatt@ieee.org SEQUENCE:16 SUMMARY:Tech Talk: Transforming enterprise quality engineering practices URL;VALUE=URI:https://events.vtools.ieee.org/m/548913 X-ALT-DESC:Description: <br /><p>The San Francisco Bay Area chapter of the IEEE Computer Society invites to our free and open Virtual Tech Talks (no IEEE membership required):</p>\n<p><strong>Eventpage:&nbsp\;</strong><a hr ef="https://www.eventbrite.com/e/transforming-enterprise-quality-engineeri ng-practices-tickets-1985250279026?aff=oddtdtcreator">https://www.eventbri te.com/e/transforming-enterprise-quality-engineering-practices-tickets-198 5250279026?aff=oddtdtcreator</a></p>\n<p><strong>Speaker:</strong><strong> &nbsp\;</strong>Jyotheeswara Reddy Gottam&nbsp\;(<a href="https://www.goog le.com/url?q=https://www.linkedin.com/in/gjreddy/&amp\;sa=D&amp\;source=ca lendar&amp\;ust=1774063921225418&amp\;usg=AOvVaw3p1OTEbWpnyMYwpkfkEjs-" ta rget="_blank" rel="noopener"><u>Connect on LinkedIn</u></a>)</p>\n<p><stro ng>Title:&nbsp\;</strong>The Triple Threat: Transforming enterprise qualit y engineering practices with Generative AI\, Predictive Analytics\, and Se lf-Healing Automation</p>\n<p><strong>Abstract:</strong>&nbsp\;Modern soft ware teams face mounting pressure to release high-quality applications fas ter while managing increasing system complexity and continuous delivery ex pectations within modern CI/CD pipelines. Traditional testing approaches o ften struggle to keep pace with rapid code changes\, expanding regression suites\, and the rising cost of maintaining automation frameworks. These c hallenges frequently lead to delayed releases\, increased testing costs\, and defects escaping into production.</p>\n<p>This presentation explores h ow the &ldquo\;Triple Threat&rdquo\; of AI-driven testing technologies&mda sh\;generative AI for test script creation\, machine learning&ndash\;based predictive defect analytics\, and self-healing automation frameworks&mdas h\;is transforming enterprise quality engineering practices.</p>\n<p>First \, generative AI accelerates test development by automatically generating test cases\, scripts\, and data from requirements\, user stories\, or code changes\, significantly reducing manual scripting effort. Second\, predic tive defect analytics powered by machine learning analyzes historical defe ct patterns\, code churn\, and previous test outcomes to identify high-ris k components and prioritize testing efforts where failures are most likely to occur. Third\, self-healing automation frameworks intelligently adapt to UI or API changes\, minimizing brittle test failures and reducing the c ostly maintenance typically associated with large automated test suites.</ p>\n<p>When deployed together\, these technologies reinforce one another: generative AI expands test coverage\, predictive analytics focuses testing on the most critical risk areas\, and self-healing automation ensures tes t suites remain resilient despite frequent application updates. Applied ac ross API testing\, functional testing\, integration testing\, and end-to-e nd testing\, this integrated approach enables organizations to modernize t heir testing strategy while improving reliability.</p>\n<p>The combined im pact allows enterprises to reduce testing costs by up to 40%\, accelerate release cycles by 30%\, and improve defect detection rates by over 50%\, d emonstrating how AI-driven testing can deliver measurable improvements in both quality and delivery speed across enterprise software systems.</p>\n< p><strong>Bio:</strong> Jyotheeswara Reddy Gottam is a Software Engineerin g Leader with over a decade of experience in the retail and e-commerce ind ustry. Currently a Senior Software Engineer at Walmart Global Tech\, he le ads end-to-end testing strategies for high-traffic marketplace platforms\, driving scalability\, reliability\, and performance for systems handling millions of daily transactions. He specializes in Gen AI\, ML\, AI agents\ , RAG\, test automation\, performance engineering\, and CI/CD enablement. Throughout his career\, he has architected scalable automation frameworks\ , reduced regression cycles significantly\, improved release velocity\, an d ensured platform stability during peak traffic events. He has also led c ross-functional initiatives across payments\, inventory\, personalization\ , and mobile platforms.</p> END:VEVENT END:VCALENDAR