OpenAI's ChatGPT s'attaque aux langages de programmation à faible ressource : SQLite, COBOL et l'effort quotidien

·Autoro Tech
OpenAI's ChatGPT s'attaque aux langages de programmation à faible ressource : SQLite, COBOL et l'effort quotidien

Look, if you've ever had to wrangle with anything outside the shiny, popular languages, you know the pain. It's not just about syntax; it's the tooling, the community, the sheer lack of decent examples. OpenAI's latest push with ChatGPT aims to make that grind a bit less brutal, specifically targeting low-resource programming languages.

They're not just talking theory. We're seeing actual evaluations for software engineering tasks, SQLite interactions, and even—get this—COBOL migration. That last one? It's a beast. Anyone who's touched legacy systems knows that migrating COBOL isn't a weekend project; it's a multi-year, soul-crushing endeavor often filled with undocumented quirks and ancient dependencies. ChatGPT stepping in here? That's a big deal, if it actually works.

The Realities of Low-Resource Languages

Don't kid yourself, this isn't a magic bullet. Even with AI, you're still dealing with inherent friction points:

  1. Limited Documentation: ChatGPT can help generate code, but if the underlying language or system has sparse documentation, the model still has less to learn from. You'll spend time debugging what should be simple. It's like asking a genius to build a house with no blueprints.
  2. Tooling Gaps: AI can write code, sure, but it can't conjure up a modern IDE, a robust debugger, or a stack of up-to-date libraries for obscure languages. You're still stuck with whatever clunky, decades-old tools exist, and that slows you down, AI or no AI.
  3. The 'Why' Problem: Sometimes, the issue isn't how to write the code, but why a particular legacy system was built that way. ChatGPT can't read the minds of engineers from 1985. Understanding those historical constraints, those forgotten business rules, that's still on you.

Where ChatGPT Could Help

For software engineering generally, having a model that understands these less-common languages means less boilerplate, quicker prototyping in niche areas, and maybe even a shot at automated refactoring where it was previously unthinkable. Think of it like having a junior dev who's surprisingly good at obscure dialects.

SQLite, for instance, is everywhere. It's embedded, it's lightweight. Getting ChatGPT to generate complex queries or schema migrations for SQLite could cut out a lot of the fiddly, error-prone manual work. It's not glamorous, but it's practical. And for those poor souls stuck on COBOL, if ChatGPT can even assist with breaking down monolithic programs or translating data structures, that's a win. It won't replace the expert, but it might make their life marginally less miserable.

This isn't about making these languages 'sexy.' It's about making them manageable. It's about taking some of the pain out of maintaining systems that aren't going anywhere anytime soon. If OpenAI can deliver on this, it'll free up a lot of developer time that's currently spent on grunt work, letting them focus on bigger problems. That's the real win.