Programming languages papers often talk about usability advantages, but the evaluation sections of these papers tend to focus on performance or semantic guarantees. What if we applied methods from user-centric research to choosing problems and evaluating solutions in PL?
Randomized algorithms and probabilistic programs play a growing role in many areas of computer science. What can we do to help ensure that these intricate programs are correct, without the bugs and flaws that plague today’s software?
Fast “metal languages” like C and C++ are important, but code is increasingly written in inefficient “irrational exuberance” languages like Python and Javascript. We in the research community can develop technology to make them run better.
