Using AI browser error stack analysis
Reading an obfuscated browser error stack and combining user environment and session behavior to judge the cause is time-consuming, even for frontend developers. WhaTap's AI browser error stack analysis (Beta) combines the error stack, environment info, error statistics, and session logs and presents the cause, impact range, and concrete fix direction at once. Available in the SaaS browser monitoring environment.
Prerequisites
- Available only in the SaaS environment
- Project member or higher permission
How to use it
Path: Analysis > Browser error tracking > error detail > Error stack analysis button
- Select the Analysis > Browser error tracking menu.
- Select the error to analyze from the list and enter the detail view.
- Click the Error stack analysis button in the error stack area.
- AI performs the analysis and presents the four-section result (a few to tens of seconds).
Analysis result structure
AI combines the collected data and presents the result in four sections.
| Section | Contents |
|---|---|
| Overall summary | A 2–3 sentence summary of the error's core |
| Cause analysis | The cause presented by severity (High/Medium/Low) |
| Impact range | Affected browsers, OSes, pages, devices, and occurrence frequency |
| Resolution | Action items by priority with code fix examples |
Severity criteria
| Severity | Criteria |
|---|---|
| High | Unhandled exception leading to app crash, data loss, or security issue |
| Medium | Impacts user experience but has a workaround |
| Low | Light issue, warning, or cosmetic |
Resolution priority
| Priority | Criteria |
|---|---|
| High | Fix immediately — affects core functionality or many users |
| Medium | Fix soon — impacts user experience |
| Low | Nice to improve — minor enhancement |
Data AI uses for analysis
| Data | Description |
|---|---|
| Error stack | Collected error stack information |
| Environment info | Browser, OS, device, and screen size |
| Error statistics | Occurrence counts by page, browser, OS, and device on the error date |
| Session logs | User behavior logs before the error (page navigation, clicks, failed API calls, etc.) |
When to use it
During frontend incident response (most useful)
Shorten browser error investigation time in step ③ Root cause analysis of the Incident response scenario.
- Select a spiking error in error tracking.
- In the error detail view, click the Error stack analysis button.
- Start from the High cause items and the Resolution section AI presents.
- Use the code fix examples to decide patch priority.
Right after a release
When errors spike in specific browsers or OSes after deployment, running AI analysis lets the Impact range section reveal which environments are concentrated at a glance. Apply this in step ② Acute regression detection of the Release verification scenario.
Periodic frontend quality review
Run AI analysis on the top N errors every quarter to build a UX improvement backlog from Medium/Low issues. Fold the results into the Performance reporting scenario.
Understand the limits of AI analysis
AI output is a first hypothesis, not the final conclusion. Use it this way:
- Cross-check AI-suggested code fix examples against your actual code context before applying.
- Analysis is based on the obfuscated stack, so code-level diagnosis has limited accuracy (source map, dSYM, and ProGuard mapping upload is not available).
- Business context (recent deploys, external dependency changes) is not visible to AI — combine with timeline info.
- Repeated analysis may not be fully consistent (LLM nature) — validate with error count and affected user count.
Adjacent AI analysis features
| Feature | When |
|---|---|
| AI active stack analysis | Identify bottlenecks in a transaction trace — AI active stack analysis |
| AI thread dump analysis | Interpret thread blocking and lock contention in an instance — AI thread dump analysis |
| AI SQL tuning guide | Identify inefficient queries and get execution-plan based improvements — AI SQL tuning guide |
| AI browser error stack analysis (this guide) | Code-level cause, impact, and fix direction for frontend errors |
| WhaTap AI Chatbot / MCP | Natural-language queries across WhaTap data — MCP integration |
Next steps
- Deeper error tracking → Browser error tracking
- Full RUM flow → Real user monitoring
- Fold into incident response → Incident response scenario