Skalpel: A constraint-based type error slicer for Standard ML

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• 作者：Vincent Rahli^a ; ^{vincent.rahli@gmail.com} ; Joe Wells^b ; John Pirie^b ; Fairouz Kamareddine^b
• 关键词：Constraint-based type inference ; Automated type inference ; Automated error diagnosis ; Type error slicing ; Improved error reports
• 刊名：Journal of Symbolic Computation
• 出版年：2017
• 出版时间：May-June 2017
• 年：2017
• 卷：80
• 期：part_P1
• 页码：164-208
• 全文大小：1590 K
• 卷排序：80

文摘

Compilers for languages with type inference algorithms often produce confusing type error messages and give a single error location which is sometimes far away from the real location of the error. Attempts at solving this problem often (1) fail to include the multiple program points which make up the type error; (2) report tree fragments which do not correspond to any place in the user program; and (3) give incorrect type information/diagnosis which can be highly confusing. We present Skalpel, a type error slicing tool which solves these problems by giving the programmer all and only the information involved with a type error to significantly aid in diagnosis and repair of type errors. Skalpel relies on a simple and general constraint system, a sophisticated constraint generator which is linear in program size, and a constraint solver which is terminating. Skalpel's constraint system can elegantly and efficiently handle intricate features such as SML's open. We also show that the Skalpel tool is general enough to deal not only with one source code file and one single error, but highlights all and only the possible locations of the error(s) in all affected files and produces all the culprit multiple program slices.