A variety of code analyzers, such as IACA, uiCA, llvm-mca or Ithemal, strive to statically predict the throughput of a computation kernel. Each analyzer is based on its own simplified CPU model reasoning at the scale of a basic block. Facing this diversity, evaluating their strengths and weaknesses is important to guide both their usage and their enhancement. We present CesASMe, a fully-tooled solution to evaluate code analyzers on C-level benchmarks composed of a benchmark derivation procedure that feeds an evaluation harness. We conclude that memory-carried data dependencies are a major source of imprecision for these tools. We tackle this issue with staticdeps, a static analyzer extracting memory-carried data dependencies, including across loop iterations, from an assembly basic block. We integrate its output to uiCA, a state-of-the-art code analyzer, to evaluate staticdeps' impact on a code analyzer's precision through CesASMe.