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ML is remarkable in providing statically typed polymorphism without the programmer ever having to write any type annotations. The cost of this parsimony is that the programmer is limited to a form of polymorphism in which quantifiers can occur only at the outermost level of a type and type variables can be instantiated only with monomorphic types.Type inference for unrestricted System F-style polymorphism is undecidable in general. Nevertheless, the literature abounds with a range of proposals to bridge the gap between ML and System F.We put forth a new proposal, FreezeML, a conservative extension of ML with two new features. First, let-and lambdabinders may be annotated with arbitrary System F types. Second, variable occurrences may be frozen, explicitly disabling instantiation. FreezeML is equipped with type-preserving translations back and forth between System F and admits a type inference algorithm, an extension of algorithm W, that is sound and complete and which yields principal types.CCS Concepts: • Theory of computation → Type structures; • Software and its engineering → Functional languages.

Termination analysis of C programs is a challenging task. On the one hand, the analysis needs to be precise enough to draw meaningful conclusions. On the other hand, relevant programs in practice are large and require substantial abstraction. It is this inherent trade-off that is the crux of the problem. In this work, we present AProVE, a tool that uses symbolic execution to analyze termination of memory-manipulating C programs. While traditionally, AProVE's focus was on the preciseness of the analysis, we describe how we adapted our approach towards a modular analysis. Due to this adaption, our approach can now also handle recursive programs. Moreover, we present further performance improvements which we developed to make AProVE scale to large programs.

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