Programming Languages: Implementations, Logics, and Programs

TL;DR: A newcumulatives constraint is presented, which generalizes the original cumulative constraint in different ways and allows modeling in an easy way workload covering, producer-consumer, and scheduling problems.

TL;DR: A mapping from extended feature models to constraint logic programming over finite domains is introduced to translate basic, cardinality-based, and extended feature model relationships, which may include complex feature-feature, feature-attribute and attribute-attribute cross-tree relationships, into constraint logic programs.

TL;DR: It is argued that solver-independent, high-level relational constraint modelling leads to a simpler and smaller language, to more concise, intuitive, and analysable models, as well as to more efficient and effective model formulation, maintenance, reformulation, and verification.

TL;DR: Focusing on novel challenges specific to local and global control, a generic algorithm for conjunctive partial deduction is described, refined into a fully automatic concrete algorithm, and proved termination and correctness.

TL;DR: The problem of the efficient detection of those variable which are deemed to be ground in the context of a reduced ordered binary-decision diagrams (ROBDD) has the effect of keeping the ROBDDs as small as possible, improving the efficiency of groundness analysis in itself.

TL;DR: This framework allows us to represent large classes of term rewriting systems, genuine parallel functions, non-determinism, communication, sharing, and hence can be used to code features from Standard ML and Concurrent ML.

TL;DR: This paper focuses on memoization in the context of handwritten program-generator generators (PGGs), where memoization gets harder, because PGGs use efficient standard representations of data at specialization time.