  Testing algorithms across a wide range of problem instances is crucial to
ensure the validity of any claim about one algorithm's superiority over
another. However, when it comes to inference algorithms for probabilistic logic
programs, experimental evaluations are limited to only a few programs. Existing
methods to generate random logic programs are limited to propositional programs
and often impose stringent syntactic restrictions. We present a novel approach
to generating random logic programs and random probabilistic logic programs
using constraint programming, introducing a new constraint to control the
independence structure of the underlying probability distribution. We also
provide a combinatorial argument for the correctness of the model, show how the
model scales with parameter values, and use the model to compare probabilistic
inference algorithms across a range of synthetic problems. Our model allows
inference algorithm developers to evaluate and compare the algorithms across a
wide range of instances, providing a detailed picture of their (comparative)
strengths and weaknesses.
