cognitive psychology. PhD 
Is anyone aware of program synthesis research on better error messages, so you can figure out how to improve your spec if synthesis fails? (perhaps related to better type errors?)
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i'm still very new to this area but producing good errors seems hard.. "you've failed to specify some constraint on a value, which is causing our search to fail... think, what implicit constraint is in your head which you haven't written down yet?"
Seems to stem from a fundamental belief in synthesis that it should be black box. Input spec, output program. But in practice, you *always* have to know how the algo works to do anything interesting. Maybe focus should change from good errors to algorithmic transparency?
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Yeah, agree! Otherwise small local moves can blow up the search and you don't know why Slight ironic that synthesis, which is used for producing more transparent algorithms than ML, suffers from this issue in its own search process
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to play devil's advocate: i think good type systems provide useful errors w/o the user needing to understand the inference algo?
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