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OpenAI models make huge gains in modernizing decades-old software with 97.9% accuracy

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OpenAI models make huge gains in modernizing decades-old software with 97.9% accuracy

OpenAI has significantly improved its models’ ability to write software in older programming languages and migrate existing code. Its latest results include 97.9 percent accuracy on a test designed to measure the migration of legacy programs.

The improvements could help businesses modernize software that remains essential to daily operations. Banks and financial institutions still rely on decades-old code, making replacement risky when developers must preserve established calculations and business rules.

COBOL remains deeply embedded in financial infrastructure. IBM estimates that it supports more than 40 percent of online banking systems, 80 percent of in-person credit card transactions, and 95 percent of ATM transactions.

Fortran also remains important in scientific computing, including weather forecasting and nuclear reactor safety analysis. Both languages have less training material available than popular alternatives such as Python and JavaScript.

OpenAI said developers and enterprise customers had reported difficulties getting coding models to work reliably with these older languages. Documentation can be incomplete, and essential business rules may exist only in the original code.

That creates problems during migration. A rewritten program might run without errors yet produce different results. COBOL, for example, can discard excess decimal digits unless a program explicitly requests rounding. Changing that behavior could affect financial calculations.

OpenAI’s COBOL migration evaluation tests whether models can translate existing programs into C++, Java, Kotlin, Python, or Rust without changing their behavior.

6 Sol. OpenAI also reported a 39 percent reduction in API costs at that setting.



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