Automated I18n Quality for Enterprise Platforms

Globalization Readiness

• Linguistic Quality
• Extensibility
• Maintainability
• Time to market
• Portability (standard based)

Reactive vs Proactive

re: fix bugs, correct translations, troubleshoot, but costumer will find issues before you

Prevent bugs, establish best practices that are global ready

Using AI out of the box

goose: Agentic vibe coding, but it dose not use ICU, does not deal with data ready for i18n.

LLM->most common, but statistically wrong.

• Not using standard region codes.
• Assumes only one language per region
• Assumes only two forms for plural
• Sloppy plural(s) construct in some languages
• No gender handling
• Embeds formatting and layout with content
• Content for all locales in a single file
• (not shown)
• Poor phone structure as raw text
• No attempt to find or use libraries for phone, address, or to CU or CLDR

Detect Issues in source content

• Before antering the translation pipeline
• Within Atlas, a plafform for managing localization workflows
• Rulebased linting
• Using 3rd party lib: ilib-lint

Github -> CI（自动实行构建） -> AWS -> Management platform ->Github/CI/Translator vendor

Detect issues in source code

• Independent of translatable content
• Much larger dataset
• Build a custom scanner
• Static Analysis + AI
• Many programming language
• Custom integrations

i18n using AI + Self-Healing

Sourcecode I18n self healing using AI study

• Scan-train-refine
• Knowend and discovered

Going forward with AI

• i18n anti patter development
• Scanning tool development
• Fine tuning results
• AI Training
• Self-healing training
• CI/CD Intergration