How can AI drive performance engineering and testing? This video explores five significant shifts in the landscape, providing insights into the impact of artificial intelligence on software testing. Discover how automation testing and performance monitoring tools are evolving, making the untestable, testable, with machine learning approaches.
One of the most noticeable shifts in how businesses approach performance testing since the widespread adoption of AI-powered software development and tools (particularly from 2023–2025) is the transition from reactive, scripted, periodic testing to proactive, predictive, continuous performance engineering.Traditional Approach (Pre-AI Dominance)
- Performance testing was largely a late-stage, siloed activity: Teams wrote rigid scripts (e.g., in JMeter or LoadRunner), ran predefined load scenarios in isolated test cycles (often just before release), manually analyzed results, and fixed issues reactively.
- It relied on static simulations that struggled to mimic real-world chaos, required heavy manual effort for scripting and root-cause analysis, and provided limited foresight — issues were often discovered only after they impacted users in production.
AI-Driven Approach (Current Shift)
- Predictive and proactive analysis — AI/ML algorithms analyze historical data, code changes, logs, and real-time telemetry to forecast bottlenecks, anomalies, and failures before they occur. This enables “shift-left” performance engineering (addressing issues early in development) and “shift-right” continuous monitoring in production.
- Intelligent, adaptive load generation — Instead of fixed scripts, AI dynamically generates and adjusts test scenarios, simulating hyper-realistic user behavior (e.g., varying traffic patterns, geographies, and edge cases) based on actual production data.
- Automated root-cause detection and self-healing — AI parses massive datasets in minutes (vs. hours/days manually), pinpoints issues (e.g., unnecessary database calls or caching misses), and even suggests or applies fixes.
- Seamless integration into CI/CD pipelines — Performance checks become continuous and real-time rather than gated events, supporting faster release cycles in Agile/DevOps environments.
- Resource optimization — AI reduces infrastructure needs (e.g., cutting load generators by 75% in some cases) and prioritizes high-risk areas, freeing engineers for higher-value work.
🔥 Like and Subscribe 🔥
Connect with me 👋
TWITTER ► https://bit.ly/3HmWF8d
LINKEDIN COMPANY ► https://bit.ly/3kICS9g
LINKEDIN PROFILE ► https://bit.ly/30Eshp7
Want to support the show? Buy Me A Coffee! https://bit.ly/3NadcPK
🔗 Links:
- Scott Moore Consulting: https://scottmoore.consulting
- The Performance Tour: https://theperformancetour.com
- SMC Journal: https://smcjournal.com
- DevOps Driving: https://devopsdriving.com
- Security Champions https://thesecuritychampions.com