BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20251030T160624Z UID:3EB1FED4-6AE6-49EE-85BD-ED14A2415064 DTSTART;TZID=America/New_York:20251029T120000 DTEND;TZID=America/New_York:20251029T130000 DESCRIPTION:Abstract\n\nIn today's rapidly evolving software engineering la ndscape\, building scalable AI-native software engineering intelligence an alytics platforms requires both technical expertise and strategic vision. With AI-generated code becoming increasingly prevalent in enterprise envir onments\, measuring its effectiveness has become critical for organization s seeking to optimize their development investments. Success depends on 3 fundamental foundations.\n\nFirst\, prioritize engineering data fidelity a nd contextual intelligence over raw metrics volume. Our AI analytics are o nly as valuable as the engineering signals they interpret. Implement sophi sticated data collection mechanisms that capture traditional metrics along side specialized tracking for AI-generated code contributions\, including origin identification\, quality assessments\, and downstream impact analys is. This foundation determines our platform's capabilities and limitations .\n\nSecond\, embrace architectural flexibility for workflow integration a nd adaptive scaling—design an analytics platform that seamlessly integra tes with existing development toolchains while handling unpredictable data patterns. Cloud-native microservices allow us to iterate on analytics com ponents independently while maintaining system resilience. Include dedicat ed modules for measuring AI code effectiveness\, tracking acceptance rates \, modification frequencies\, and security patterns.\n\nFinally\, center d evelopment around engineering team productivity and developer experience. Implement tight feedback loops with development teams to understand how th ey consume intelligence insights.\n\nThe most impactful AI-native platform s strike a balance between cutting-edge AI capabilities and pragmatic engi neering principles. We focus on solving today's delivery challenges and ar chitecting tomorrow's autonomous workflows.\n\nCo-sponsored by: Avimanyou Vatsa\n\nSpeaker(s): Naveen Kumar\, \n\nRoom: 205\, Bldg: Becton Hall\, 9 60 River Road\, Teaneck\, New Jersey\, United States\, 07666\, Virtual: ht tps://events.vtools.ieee.org/m/503289 LOCATION:Room: 205\, Bldg: Becton Hall\, 960 River Road\, Teaneck\, New Je rsey\, United States\, 07666\, Virtual: https://events.vtools.ieee.org/m/5 03289 ORGANIZER:avatsa@fdu.edu SEQUENCE:2 SUMMARY:What would they like to hear more of? I am thinking: Building a sca lable Generative AI product URL;VALUE=URI:https://events.vtools.ieee.org/m/503289 X-ALT-DESC:Description: <br /><h1><span style="font-size: 11.0pt\;">Abstrac t</span></h1>\n<p class="MsoNormal" style="text-align: justify\; mso-pagin ation: widow-orphan\; background: white\; text-autospace: ideograph-numeri c ideograph-other\; margin: 0in 67.5pt .0001pt 58.5pt\;"><span style="colo r: black\; border: none windowtext 1.0pt\; mso-border-alt: none windowtext 0in\; padding: 0in\;">In today's rapidly evolving software engineering la ndscape\, building scalable AI-native software engineering intelligence an alytics platforms requires both technical expertise and strategic vision. With AI-generated code becoming increasingly prevalent in enterprise envir onments\, measuring its effectiveness has become critical for organization s seeking to optimize their development investments. Success depends on 3 fundamental foundations.</span></p>\n<p class="MsoNormal" style="margin-le ft: 58.5pt\; mso-pagination: widow-orphan\; background: white\; text-autos pace: ideograph-numeric ideograph-other\;"><span style="font-size: 11.5pt\ ; color: #242424\;">&nbsp\;</span><span style="color: black\; border: none windowtext 1.0pt\; mso-border-alt: none windowtext 0in\; padding: 0in\;"> First\, prioritize engineering data fidelity and contextual intelligence o ver raw metrics volume. Our AI analytics are only as valuable as the engin eering signals they interpret. Implement sophisticated data collection mec hanisms that capture traditional metrics alongside specialized tracking fo r AI-generated code contributions\, including origin identification\, qual ity assessments\, and downstream impact analysis. This foundation determin es our platform's capabilities and limitations.</span></p>\n<p class="MsoN ormal" style="text-align: justify\; mso-pagination: widow-orphan\; backgro und: white\; text-autospace: ideograph-numeric ideograph-other\; margin: 0 in 67.5pt .0001pt 58.5pt\;"><span style="color: black\; border: none windo wtext 1.0pt\; mso-border-alt: none windowtext 0in\; padding: 0in\;">Second \, embrace architectural flexibility for workflow integration and adaptive scaling&mdash\;design an analytics platform that seamlessly integrates wi th existing development toolchains while handling unpredictable data patte rns. Cloud-native microservices allow us to iterate on analytics component s independently while maintaining system resilience. Include dedicated mod ules for measuring AI code effectiveness\, tracking acceptance rates\, mod ification frequencies\, and security patterns.</span></p>\n<p class="MsoNo rmal" style="text-align: justify\; mso-pagination: widow-orphan\; backgrou nd: white\; text-autospace: ideograph-numeric ideograph-other\; margin: 0i n 63.0pt .0001pt 58.5pt\;">&nbsp\;</p>\n<p class="MsoNormal" style="text-a lign: justify\; mso-pagination: widow-orphan\; background: white\; text-au tospace: ideograph-numeric ideograph-other\; margin: 0in 63.0pt .0001pt 58 .5pt\;"><span style="color: black\; border: none windowtext 1.0pt\; mso-bo rder-alt: none windowtext 0in\; padding: 0in\;">Finally\, center developme nt around engineering team productivity and developer experience. Implemen t tight feedback loops with development teams to understand how they consu me intelligence insights.</span></p>\n<p class="MsoNormal" style="text-ali gn: justify\; mso-pagination: widow-orphan\; background: white\; text-auto space: ideograph-numeric ideograph-other\; margin: 0in 63.0pt .0001pt 58.5 pt\;">&nbsp\;</p>\n<p class="MsoNormal" style="text-align: justify\; mso-p agination: widow-orphan\; background: white\; text-autospace: ideograph-nu meric ideograph-other\; margin: 0in 63.0pt .0001pt 58.5pt\;"><span style=" color: black\; border: none windowtext 1.0pt\; mso-border-alt: none window text 0in\; padding: 0in\;">The most impactful AI-native platforms strike a balance between cutting-edge AI capabilities and pragmatic engineering pr inciples. We focus on solving today's delivery challenges and architecting tomorrow's autonomous workflows</span><span style="color: black\; mso-col or-alt: windowtext\; mso-bidi-font-weight: bold\;">.</span></p> END:VEVENT END:VCALENDAR