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mikedodds | 3 years ago
The downside is that the space is quite fragmented and a lot of tools have a high skill bar. If I was starting out, I'd probably focus on static analysis (eg. Infer or something similar - https://github.com/facebook/infer) because those tools tend to be easier to learn, and they have the potential to scale to really big systems. In contrast, Coq is a fine tool, but most people learn it by going to grad school which isn't useful short term career advice.
There are lot of great interviews with practitioners on the Galois podcast, Building Better Systems - that might be a good place to start exploring: https://www.stitcher.com/show/building-better-systems
nextos|3 years ago
When I graduated there were some interesting opportunities, but the field looked a bit too stale and I ended up moving into a slightly different research area (probabilistic model checking and probabilistic inference in general).
There is a lot of hype around theorem proving, particularly with dependent types. As you say static analysis (and model checking) might be a better bet due to scalability, unless transformer architectures get to the point where writing proofs can be done much faster? What do you think about more practical approaches such as Dafny?
mikedodds|3 years ago
schoen|3 years ago
I started learning a little bit of Lean from the Natural Number Game, and subsequently worked through Logical Foundations (with the generous help of one of the coauthors!), and have continued learning Coq afterward.
Are there books or sites or exercises out there for "people who've already learned Isabelle and now would like to learn Coq", "people who've already learned Coq and now would like to learn Lean", "people who've already learned Coq and now would like to learn tools and frameworks for verifying protocols", "people who've learned OCaml or Haskell and now would like to learn Coq", etc.?
jnash|3 years ago