  Recent renewed interest in optimizing and analyzing floating-point programs
has lead to a diverse array of new tools for numerical programs. These tools
are often complementary, each focusing on a distinct aspect of numerical
programming. Building reliable floating point applications typically requires
addressing several of these aspects, which makes easy composition essential.
This paper describes the composition of two recent floating-point tools:
Herbie, which performs accuracy optimization, and Daisy, which performs
accuracy verification. We find that the combination provides numerous benefits
to users, such as being able to use Daisy to check whether Herbie's unsound
optimizations improved the worst-case roundoff error, as well as benefits to
tool authors, including uncovering a number of bugs in both tools. The
combination also allowed us to compare the different program rewriting
techniques implemented by these tools for the first time. The paper lays out a
road map for combining other floating-point tools and for surmounting common
challenges.
