Data Pipeline Transformation

Situation

Our client, a global financial services information services company, relied on a legacy code translator module within its application to translate code bases from multiple remote business units. The existing system was limited in scalability, language support, and performance.

The legacy platform struggled with error handling, efficiency, and the ability to extend into new target languages, which slowed down development cycles and increased maintenance costs. As the client aimed to modernize its platform and support broader code translation use cases, it required a more robust, extensible, and performant solution that would improve accuracy while reducing overhead.

"Nuvolant delivered a modern, extensible code translator that improved our accuracy, speed, and confidence in supporting multiple languages."

— Global Information Company, VP, Engineering

Solution

Nuvolant proposed a full re-architecture and modernization of the code translator, migrating it to a Python-based framework leveraging Python Lex-Yacc (PLY). The new architecture was designed with modularity and extensibility at its core, separating concerns into distinct stages: lexical analysis, parsing, abstract syntax tree (AST) generation, and code translation.

The solution introduced a streamlined process for converting SPECS code to Java, while also laying the groundwork for expanding into additional languages. Nuvolant built AST transformation logic with pruning, abstraction, and annotation to ensure translations preserved semantic accuracy, with custom translation rules for constructs such as control flow, functions, and variable assignments.

Advanced error handling was embedded throughout the process, capturing syntax, semantic, translation, and runtime errors with clear feedback for users. Performance was optimized through caching mechanisms, AST optimizations, and parallel processing, enabling efficient handling of large codebases. The entire project followed a structured 90–110 day timeline, with clear milestones spanning design, parser development, translation mechanism implementation, optimization, and delivery.

Outcome

The re-built and re-architected code translator enabled faster, more reliable, and scalable code translations. With improved error reporting and performance tuning, the tool significantly reduced translation errors and processing times. Developers gained confidence in the accuracy of output code, while the modular design made it easier to add support for new languages and extend functionality as requirements evolved.

By delivering a Python-based, future-ready translation engine, Nuvolant not only solved immediate pain points but also positioned the client with a flexible platform capable of supporting its long-term goals.