AI-Driven Strategies for Scalable & Resilient Performance Engg.
summary
Claims an AI Accelerated PE pipeline that auto-ingests NFRs, generates JMeter/LoadRunner scripts, analyses APM data (Splunk, Grafana, NewRelic, Dynatrace, Datadog) and autowrites test summaries; recommends early performance single‑user checks in dev, component at 1/3 production scale and avoids fixing response issues only at integrated tests because integrated runs cost ~40x. Presents DMSI analytics on 200+ projects / 18,000 defects showing 62% repetitive defects and a discovery that fixes take 30 minutes vs 4.5 days to hit the right engineer.
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chapters (13)
- 0:00AI-driven performance engineering intro
- 8:06AI transforming quality engineering
- 16:41Shift-left in performance testing
- 22:27Metrics and root cause analysis
- 27:05Upskilling in QE
- 33:24Tool selection and automation
- 37:39Continuous performance testing phases
- 40:09Defect management intelligence
- 43:41Q&A: AI vs human intuition
- 45:49Q&A: Cost vs coverage
- 47:33Q&A: Fair AI models
- 49:01Q&A: ROI in automation
- 49:43Closing remarks
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