An Application of Constraint Propagation to Data-Flow Analysis

The optimized compilation of Constraint Logic Programming (CLP) languages can give rise to impressive performance improvements, even more impressive than the ones obtainable for the compilation of Prolog. On the other hand, the global analysis techniques needed to derive the necessary information can be significantly more complicated than in the case of Prolog. The original contribution of the present work is the integration of approximate inference techniques, well known in the field of artificial intelligence (AI), with an appropriate framework for the definition of non-standard semantics of CLP. This integration turns out to be particularly appropriate for the considered case of the abstract interpretation of CLP programs over numeric domains. One notable advantage of this approach is that it allows to close the often existing gap between the formalization of data-flow analysis in terms of abstract interpretation and the possibility of efficient implementations. Towards this aim we identified a class of approximate deduction techniques from AI and a semantic framework general enough to accommodate the corresponding approximate constraint systems