Paper proposes a LLM-based system to convert legacy scientific code with built-in verification mechanisms.
https://arxiv.org/abs/2410.24119
Original Problem 🎯:
Legacy Fortran code translation to C++ in scientific computing is time-consuming and error-prone. Manual conversion takes years, requiring deep expertise in both languages and careful handling of interoperability issues.
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Solution in this Paper 🛠️:
→ Code-Scribe combines prompt engineering with user supervision for efficient code conversion
→ The tool uses four main commands: Index (maps project structure), Inspect (interactive code querying), Draft (preliminary C++ code generation), and Translate (AI-powered conversion)
→ Implements Retrieval-Augmented Generation (RAG) to provide context about external functions and avoid declaration errors
→ Uses chat completion templates to guide LLMs in generating accurate translations
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Key Insights 🔍:
→ Incremental translation with interface layers is more effective than bulk conversion
→ LLMs require specific context and constraints to avoid hallucinations in code generation
→ Developer supervision remains crucial for verification despite AI automation
→ Pattern recognition in source code improves translation accuracy
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Results 📊:
→ Developer productivity increased from 2-3 files/day to 10-12 files/day
→ GPT-4 showed best performance with 2.5 minutes review time
→ CodeLlama-7B and Mistral-7B required more intervention, averaging 13 minutes review time
