Validating experimental results in the field of storage systems is a challenging task, mainly due to the many changes in software and hardware that computational environments go through. Determining if an experiment is reproducible entails two separate tasks: re-executing the experiment and validating the results. Existing reproducibility efforts have focused on the former, envisioning techniques and infrastructures that make it easier to re-execute an experiment. In this position paper, we focus on the latter by analyzing the validation workflow that an experiment re-executioner goes through. We notice that validating results is done on the basis of experiment design and high-level goals, rather than exact quantitative metrics. Based on this insight, we introduce a declarative format for specifying the high-level components of an experiment as well as describing generic, testable conditions that serve as the basis for validation. We present a use case in the area of distributed storage systems to illustrate the usefulness of this approach.