Update: APRIL FOOLS!
by Kaue Silveira
Here at Google, we invest heavily in development productivity research. In fact, our TDD research group now occupies nearly an entire building of the Googleplex
. The group has been working hard to minimize the development cycle time, and we’d like to share some of the amazing progress they’ve made. The Concept
In the ways of old, it used to be that people wrote tests for their existing code. This was changed by TDD (Test-driven Development), where one would write the test first and then write the code to satisfy it. The TDD research group didn’t think this was enough and wanted to elevate the humble test to the next level. We are pleased to announce the Real
TDD, our latest innovation in the Program Synthesis
field, where you write only the tests and have the computer write the code for you!
The following graph shows how the number of tests created by a small feature team grew since they started using this tool towards the end of 2013. Over the last 2 quarters, more than 89% of this team’s production code was written by the tool! See it in action:
Test written by a Software Engineer:
self.generator = link_generator.LinkGenerator()
expected = ('https://frontend.google.com/advancedSearchResults?'
actual = self.generator.GetLinkFromIDs(set((1346270, 1310696, 1288585)))
Code created by our tool:
_URL = (
def GetLinkFromIDs(self, ids):
result = 
for id in sorted(ids):
result.append('%s ' % id)
return self._URL + urllib.quote_plus(''.join(result))
Note that the tool is smart enough to not generate the obvious implementation of returning a constant string, but instead it correctly abstracts and generalizes the relation between inputs and outputs. It becomes smarter at every use and it’s behaving more and more like a human programmer every day. We once saw a comment in the generated code that said "I need some coffee". How does it work?
We’ve trained the Google Brain
with billions of lines of open-source software to learn about coding patterns and how product code correlates with test code. Its accuracy is further improved by using Type Inference
to infer types from code and the Girard-Reynolds Isomorphism
to infer code from types.
The tool runs every time your unit test is saved, and it uses the learned model to guide a backtracking search for a code snippet that satisfies all assertions in the test. It provides sub-second responses for 99.5% of the cases (as shown in the following graph), thanks to millions of pre-computed assertion-snippet pairs stored in Spanner
for global low-latency access. How can I use it?
We will offer a free (rate-limited) service that everyone can use, once we have sorted out the legal issues regarding the possibility of mixing code snippets originating from open-source projects with different licenses (e.g., GPL-licensed
tests will simply refuse to pass BSD-licensed
code snippets). If you would like to try our alpha release before the public launch, leave us a comment!