This is "studying computer science" now? Vibe coding is easy and fun, but why spend 4 years and a small fortune to study it when practically anyone can pick it up over a weekend?
Even those are just going to be outsourced to AI by the "students"
But we studied algorithms for them anyway.
Although making students write it on paper (as described) feels extreme. Why not just make them write the code manually on a regular IDE, see how it compiles, etc.?
We need more people who understand the software theories/models/mathematics/etc. of Computer Science and can develop large-scale systems via "Practical Software Engineering". Otoh, we need less of people who are mere Computer Programmers.
I am not sure how many here on HN, are familiar with Computer-Aided Software Engineering (CASE - https://en.wikipedia.org/wiki/Computer-aided_software_engine...) methodologies/tools/techniques and how they were used for Round-Trip Engineering (RTE - https://en.wikipedia.org/wiki/Round-trip_engineering). That unrealized promise can now be realized using AI tools.
The idea was that you would have a Specification (Formal/Informal) defined by problem domain experts in some notation (textual/pictorial), have the tool generate code and the resulting artifact Verified (Formal/Informal) against the specification. A change in the specification will update the generated code and needed verification steps (and vice-versa) seamlessly.
This is what a current CS graduate needs to focus on (for employment purposes); viz.
1) The full Software Engineering process with focus on Requirements Specification and Verification. There are lots of notations/techniques available which you need to become familiar with. Some examples are Parnas Tables (https://cs.uwaterloo.ca/~jmatlee/Talks/Parnas01.pdf), Decision Tables (https://en.wikipedia.org/wiki/Decision_table), Structured English (https://en.wikipedia.org/wiki/Structured_English) etc.
2) Formal Methods for Specification and Verification. Focus on the complete end-to-end methodology like for example; The B-Method - https://en.wikipedia.org/wiki/B-Method Another example is to use Prolog for system specification.
3) Devising a methodology to trace the specification through the AI generated code using the above. For example, you can have the agent map the specifications to preconditions/postconditions/invariants in the runtime code and then have it extract those into appropriate functional documentation so you can see how functional requirements are enforced.
4) Understanding "Correctness-By-Construction"/"Design-By-Contract" approaches to software development which must be used for AI code generation.
5) Your AI prompt is now the specification. It would be a mix of Formal and Informal since only Formal can assure traceability. You have to find the balance for yourself and your problem.
The above are the main points. Each can be detailed further based on your CS study ;-)
The other main issue that I see, is that even if there is a formally verified specification, at the moment, LLMs will not respect it perfectly. As long as LLMs are not able to non-deterministically follow a spec, the technology is not good enough.
A part from that, imo, in this age we should focus more on the mathematical aspect of computations, and I think we need to develop novel theories that take into account the non deterministic nature of LLMs in the process. I'm not sure this will ever work by merely extending current practices, as software design practices are extremely poorly defined from an engineer point of view. Just extending them by including randomeness does not seem a good idea.
I mainly pointed out some of the important Software Engineering methodologies/techniques to be studied and adapted for use with AI. Earlier, they were encompassed/expressed-by specific software tools (CASE/Formal Method tool etc.) which may/may-not be used alongside AI. You study and extract the principles/concepts/ideas behind those tools and adapt them for use with the more powerful all-in-one AI tool.
Contrary to your claim, Waterfall methodologies (mainly the stages and iteration amongst them) have not failed but are now uniquely adaptable for AI. Most people on HN have a very wrong idea of what a Waterfall and its related Spiral Model are - https://news.ycombinator.com/item?id=45145706
The Formal Methods mentioned above already encompass "mathematical aspect of computations" and more. The non-deterministic nature of LLM output is taken care of by "Correct-by-Construction"/"Design-by-Contract" approaches which are based on Set Theory/Predicate Logic. LLMs must be made to generate code along with correctness proof using the above (Dijkstra's methodology). See the Dafny language for some background - https://dafny.org/
To summarize; understand classical Software Engineering methodologies/techniques since they focused on end-to-end SDLC of which programming/coding was only a small part and use them around a AI tool. Add in formal method techniques (this is a huge field in itself eg. model checking, theorem proving etc.) for both Specification and Verification. You can use the AI tool itself for all stages.
For example; you can take a unstructured requirements document from a client and have the AI tool generate a multi-level decision table like described in https://news.ycombinator.com/item?id=38821708 From this you can have AI generate modular state machine implementation code with pre/post/inv conditions directly mapping to the decision table. The decision table can also be verified by a model checker either directly or by transforming into a verifiable state-transition model. Add in test cases etc. and you have a end-to-end system with guaranteed traceability.
Agree, we are in the stone age in software design and dev. We have not figured out a good way to communicate the design of complex systems in a way the business can understand.
Do not learn computer science these days. We all are f***
And I’m a guy who solidly believes in understanding the fundamentals.
