Automating Abstract Interpretation
Thomas Reps and Aditya Thakur
Abstract interpretation has a reputation of being a kind
of ``black art,'' and consequently difficult to work with.
This paper describes a twenty-year quest by the first author
to address this issue by raising the level of automation
in abstract interpretation.
The most recent leg of this journey is the subject of
the second author's 2014 Ph.D. dissertation.
The paper discusses several different approaches to
creating correct-by-construction analyzers.
Our research has allowed us to establish connections between this
problem and several other areas of computer science,
including automated reasoning/decision procedures,
concept learning, and constraint programming.
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