The domain of positive Boolean functions, Pos, is by now well established for the analysis of the variable dependencies that arise within logic programs. Analyses based on Pos that use binary decision diagrams (BDDs) have been shown to be efficient for a wide range of practical programs. However, independent of the representation, a Pos analysis can never come with any efficiency guarantees because of its potential exponential behaviour. This paper considers groundness analysis based on a simple subdomain of Pos and compares its precision with that of Pos.

The domain of positive Boolean functions, P os, is by now well established for the analysis of the variable dependencies that arise within logic programs. Analyses based on P os that use binary decision diagrams have been shown to be eecient for a wide range of practical programs. However, independent of the representation, assuming that P 6 = N P , an (unwidened) P os analysis can never come with any ee-ciency guarantees because of its potentially explosive behaviour. This paper proposes a simple widening for (goal-dependent) groundness analysis of logic programs that guarantees scalability and eeciency. Experimental results indicate that the widening induces only a very small loss of precision.

The domain of positive Boolean functions, P os, is by now well established for the analysis of the variable dependencies that arise within logic programs. Analyses based on P os that use binary decision diagrams have been shown to be eecient for a wide range of practical programs. However, independent of the representation, assuming that P 6 = N P , an (unwidened) P os analysis can never come with any ee-ciency guarantees because of its potentially explosive behaviour. This paper proposes a simple widening for (goal-dependent) groundness analysis of logic programs that guarantees scalability and eeciency. Experimental results indicate that the widening induces only a very small loss of precision.