Objective

Design and implement a smart, adaptive performance testing framework that reduces testing cost and time by selectively executing relevant tests using rule-based, AI-driven strategies, and historical performance data - while maintaining high confidence in test coverage and regression detection.

Key Features

• Performance Analysis Integration (Jenkins level)

  • Make pass/fail decisions based on performance thresholds and historical data from TRSS (Test Result Summary Service) API.

  • Aggregate and present concise perf metrics in the console

• Iteration Control

  • Use confidence intervals or statistical significance to determine if more iterations are needed.

  • If results are consistent early, exit early. Reduce test and baseline iterations.

• Tiered Testing Strategy

  • Tier 1: Fast, lightweight tests on pull requests. (Can be auto-triggered via github workflow)

  • Tier 2: Medium-scale tests on merges or flagged commits.

  • Tier 3: Full-scale perf suite for release candidates.

• Auto Perf Checks in Code Review

  • static analysis or code review bots (or similar to bug prediction)

• Targeted Test Selection***

  • Rule based: Combine with commit metadata (e.g., files touched, feature impacted) to reduce the test matrix.

  • AI: model-based input selection or usage data to run performance tests only on high-impact scenarios.

***Would want to categorize benchmarks where possible, some benchmarks exercise certain java packages extensively (example, some are heavy for file processing).