In recent years software has grown in its complexity and many software packages now have a large number of dependencies.
Typical software packages may depend on tens to hundreds of other packages.
As this complexity continues to grow it becomes more and more difficult to find compatible versions in the dependency graph.
Many package managers rely on logic programming and SAT solvers to resolve version constraints, yet while these version constraints remain hand-annotated there will continue to be errors from version conflicts.
Additionally, these constraints may not hold across different architectures, OS's, and/or compilers.
In this talk we demonstrate how machine learning models that predict the probability of dependency graphs successfully building can be integrated into the package manager Spack's version selection mechanism.
We discuss how to integrate probabilistic build information into Spack's Answer Set Programming (ASP) solver via a probabilistic variant of ASP, Plingo.
Additionally, we present several means of extrapolating to new versions as they are added to the package manager.
Finally, we demonstrate and discuss the effectiveness of using probabilistic information in version selection.