Git-Based Localization

An agile practice that integrates translation directly into the CI/CD pipeline. When a developer pushes new source strings to a Git branch, an automated webhook triggers the translation management system to fetch the content, run machine translation, and open a pull request with the translated files. This eliminates the traditional localization 'hand-off' and enables simultaneous multilingual release. Key components include automated repository syncing, branch-based workflow triggers, and linguistic quality gates that run as part of the build process.

A database that stores previously translated source-target segment pairs at the sentence or paragraph level. In a Git-based workflow, the TM is queried during the automated translation step to identify exact or fuzzy matches. When a developer updates only 10% of a JSON resource file, the TM supplies the unchanged 90% from historical translations, ensuring consistency across versions and reducing translation costs. The TM itself is often versioned or synced as a binary asset within the localization pipeline.

An automated mechanism that ensures brand-critical terms are translated consistently across all localized files in a repository. A termbase defines approved translations for specific keys (e.g., 'checkout' must always translate to 'Kasse' in German). During the pull request stage, a CI check scans the translated resource files and flags any deviation from the glossary, blocking the merge until corrected. This prevents the gradual erosion of terminological consistency that occurs when multiple translators or MT engines work on the same codebase over time.

A pre-translation testing technique that simulates localization by generating pseudo-translated resource files in a separate Git branch. The process pads strings with accented characters, brackets, and length multipliers to surface i18n bugs before real translation begins. Common issues caught include:

• Hardcoded strings that were missed during externalization
• UI elements that break with expanded text
• Encoding errors with non-ASCII characters
• Concatenation logic that fails with bidirectional text Running pseudolocalization as a CI job on every commit prevents costly rework after actual translations are delivered.

A machine learning task that predicts the quality of a machine translation output without requiring a human reference translation. In a Git-based workflow, a QE model runs as a CI check on incoming translation pull requests, assigning a confidence score at the sentence or document level. Segments scoring below a threshold are automatically flagged for human post-editing, while high-confidence segments are approved for merge. This creates a triage pipeline that optimizes human translator effort, focusing it only on the content that machines cannot handle reliably.