Despite advances in repository-level code translation, automated type resolution across differing type systems and translation validation (functional-equivalence checking) remain challenging. This paper proposes MockingBird, combining context-aware type resolution with mock-based isolated validation. MockingBird retrieves API documentation and contextual code information per source-language type, prompting an LLM to resolve precise cross-language type mappings, reducing the manual effort of existing approaches. Using this automatically constructed type map, MockingBird employs custom serialization/deserialization to generate equivalent target-language mock objects, validating each translation unit in isolation without test translation, eliminating test-overfitting risks and translation challenges. On 10 Java-to-Python tasks from the AlphaTrans benchmark, MockingBird improves functional equivalence from the state-of-the-art 25.14% to 43.10%, with no manual type-mapping correction needed. A 700-fragment study shows MockingBird achieves 0 false positives and 0.9% false negatives, substantially outperforming GraalVM (2.4% false positives, 7.6% false negatives) and MatchFixAgent (11.0% combined mislabel rate). The pipeline is also LLM-agnostic, maintaining these gains with both DeepSeekCoder-33B and the smaller Devstral-24B.

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