A Generic Framework for the Analysis and Specialization of Logic Programs

The relationship between abstract interpretation [2] and partial evaluation [5] has received considerable attention and (partial) integrations have been proposed interpretation perspectives. Abstract interpretation-based analyzers (such as the CiaoPP analyzer [9, 4]) generally compute a program analysis graph [1] in order to propagate (abstract) call and success information by performing fixpoint computations when needed. On the other hand, partial deduction methods [7] incorporate powerful techniques for on-line specialization including (concrete) call propagation and unfolding. In this work we propose what we argue is the first generic framework for the efficient and precise integration of abstract interpretation and partial deduction from an abstract interpretation perspective, and which combines the best of both worlds. As starting point, we consider state-of-the-art algorithms for context-sensitive, polyvariant abstract interpretation [9, 4]. The central idea in this novel framework is to extend such algorithms, which already incorporate succes