  Deep neural networks have been increasingly used in software engineering and
program analysis tasks. They usually take a program and make some predictions
about it, e.g., bug prediction. We call these models neural program analyzers.
The reliability of neural programs can impact the reliability of the
encompassing analyses. In this paper, we describe our ongoing efforts to
develop effective techniques for testing neural programs. We discuss the
challenges involved in developing such tools and our future plans. In our
preliminary experiment on a neural model recently proposed in the literature,
we found that the model is very brittle, and simple perturbations in the input
can cause the model to make mistakes in its prediction.
