RESEARCH REPORT 2025

Self-Healing Locators Research Study

Comprehensive research analyzing self-healing locator approaches across major automation frameworks. Explore methodology, preliminary projections, and planned publication.

Benchmark Methodology Overview

Test Applications

  • React e-commerce store (OSS)
  • Angular enterprise dashboard (OSS)
  • Vue admin panel (OSS)
  • ≥300 element actions per application

Systematic Mutations

  • ID/attribute & text/i18n changes
  • DOM re-parenting & wrapper elements
  • Responsive breakpoints & Shadow DOM
  • ARIA/role shifts & class name churn

Test Matrix

10 seeds × 3 browsers × 3 CI repeats = 90 runs per strategy

Frameworks Evaluated

  • Selenium WebDriver
  • Playwright
  • Cypress
  • Puppeteer
  • Custom AI solutions

Reporting will include hardware/versions, medians, and 25–75th percentiles for all metrics.

Projected Performance (Pre-Benchmark)

Directional projections based on internal pilots. Sub-second recovery is possible with warmed models; cold starts may be higher.

StrategyProjected Success Rate*Projected Recovery Latency*Projected Maintenance (min / 100 runs)*
Static CSS/XPath60–70%3–5 sHigh (45–70)
Dynamic Attributes70–80%2–4 sMedium (25–45)
ML-Based Healing80–90%1–3 sMedium (15–30)
Agentic AI + Context Retrieval90–98%~1–2 s†Very Low (5–15)

*Estimates; will be replaced by measured medians and IQR. †Warmed models; cold starts may be higher.

What the Final Report Includes

  • Verified performance benchmarks
  • Statistical analysis + raw data
  • Code samples & framework guides
  • ROI calculation templates
  • Full methodology & appendices

Technical Clarifications

  • Context Retrieval: DOM snapshots, prior run history, design tokens, analytics.
  • Performance Notes: First-run vs warm-cache latencies reported separately.
  • Limits: Captchas, 2FA, canvas-only UIs called out.
  • Open-source helper library (MIT) planned alongside report.

Join Research Updates

Get the completed benchmark report in Q2 2025. Early access for subscribers.