Self Healing - Timely and Just the Right Amount
A three-tier self-healing strategy that minimises test failures, reduces maintenance overhead, and ensures consistent execution accuracy while controlling costs.
Modern test automation faces significant challenges maintaining script stability as applications evolve. Nogrunt implements a three-tier self-healing strategy to minimise failures, reduce maintenance overhead, and ensure consistent execution accuracy while controlling costs.
Static Self-Healing with Multi-Locator Strategy
The first tier captures eight unique locators for each element during scripting - XPath, CSS selectors, IDs, names, class attributes, and text-based identifiers. A waterfall execution model tries the primary locator first, then falls back sequentially.
- Locators generated through bot-based automated scanning or AI-based generative analysis.
- Cross-run validation compares located elements against previous successful executions.
DOM Analysis and Contextual Recovery
When waterfall locators fail, the second tier inspects the page source directly. Contextual proximity rules identify elements closest to previously known locations, maintaining history and behavioural context for each element across runs.
AI-Driven Self-Healing
Reserved for complex cases where static and DOM methods fail, the third tier evaluates historical element interactions, semantic context, and functional intent to locate the correct element.
Cost-Benefit Balance
The layered approach optimises costs by reserving AI for last-resort scenarios. Low-cost static methods handle the majority of healing, DOM analysis catches the next tier, and AI steps in only when both fail - giving you accuracy without unnecessary spend.