AI Coding Assistants for COBOL, SQLite and Other Low-Resource Languages

B Tecky analysis · 2026-10-19 — Code assistants need to understand legacy ecosystems, not just popular modern frameworks.

Low-resource programming languages and legacy systems often have less training data, fewer public examples and specialized conventions. This makes code generation harder: an assistant may produce plausible syntax that does not compile in a particular compiler, runtime or enterprise environment.

A useful evaluation must go beyond trivia questions. Include real repository tasks, compiler feedback, database behavior, dependency constraints, security checks and maintainability. For migration work, compare behavior before and after each change using regression tests and representative production-like data.

A practical workflow is to ask the assistant to explain the existing code first, identify dependencies, propose a small change, write tests, and only then implement. Require a human reviewer for data migrations, financial calculations and changes to critical systems.

# A cautious legacy-code change loop
make test
make lint
make build
# Review the diff before committing
 git diff --check

For teams maintaining COBOL, SQL or older business systems, AI can help document unfamiliar modules, generate test scaffolding and explain control flow. It should not be allowed to rewrite large sections without traceable tests. Keep compiler versions, runtime assumptions and data formats explicit.

Practical recommendation: use coding assistants as pair programmers and documentation helpers. Evaluate them on your own codebase and measure defects introduced—not only lines of code generated.

Source: OpenAI Research.

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