Academic prototyping (invited tutorial)

Summary

Much of our research requires building tools to evaluate and demonstrate new approaches. Yet, tool building can take large amounts of time and resources. And it brings risks: The original idea might not work; rendering all efforts futile. And after the student in charge has left, the tool becomes a maintenance problem. In this tutorial, I present tools and techniques for the quick creation of prototypes for academic research. Building prototypes in and for Python is at least ten times faster than traditional engineering in and for C or Java; it thus frees lots of time for creating and refining approaches. Using Jupyter Notebooks to code and run experiments saves rationales, settings, examples, and results in self-contained documents that can be read and reused easily by other students and researchers. Once the basics of the new approach are settled (and only then!) can one go and port the new approach to “traditional” languages – or move on to the next challenge. You don’t believe me? Watch me illustrate this approach by building a symbolic fuzzer (think KLEE) for Python from scratch, live coding the capturing and solving of path conditions in less than 20 minutes. From there, we will determine what it is that makes languages like Python and tools like Jupyter Notebooks so suitable for academic prototyping, and the consequences this has (or should have) for organizing our research.