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Code generation helps developers improve software development productivity. But statistical machine learning, like large language models (LLMs), doesn't reliably generate accurate source code and requires human review. Symbolic approaches toward Natural Language Process (NLP), pursued since the 1950s for higher precision, have seen limited adoption because of its weak learning capacity.
Froglingo, a Turing-complete language system, bridges symbolic precision with machine learning by introducing a learnable symbolic database language, the Enterprise-Participant (EP) data model, for accurate natural language processing (NLP) and code generation. EP reaches the mathematical upper bound in what applications including NLP and code generation can be constructed through learning.
Froglingo builds understanding by automatically mapping natural language utterance to manually constructed symbolic templates using a hybrid of Froglingo's native language and natural language. Upon a finite and predictable amount of sample utterances, EP guarantees a code generation application that understands natural language and precisely generates code that can be immediately executed to trustfully implement peoples' instructions.
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