The mechanism for declaring datatypes to model data structures in functional programming languages such as Standard ML and Haskell can offer both convenience in programming and clarity in code. With the introduction of dependent datatypes in DML, the programmer can model data structures more accurately, capturing more program invariants. In this paper, we study some practical aspects of dependent datatypes that affect both type-checking and compiling pattern matching as well as datatype representation. The results, which have already been tested, demonstrate that dependent datatype can not only offer various programming benefits but also lead to performance gains, yielding a concrete case where safer programs run faster.
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