  Code completion is one of the killer features of Integrated Development
Environments (IDEs), and researchers have proposed different methods to improve
its accuracy. While these techniques are valuable to speed up code writing,
they are limited to recommendations related to the next few tokens a developer
is likely to type given the current context. In the best case, they can
recommend a few APIs that a developer is likely to use next. We present FeaRS,
a novel retrieval-based approach that, given the current code a developer is
writing in the IDE, can recommend the next complete method (i.e., signature and
method body) that the developer is likely to implement. To do this, FeaRS
exploits "implementation patterns" (i.e., groups of methods usually implemented
within the same task) learned by mining thousands of open source projects. We
instantiated our approach to the specific context of Android apps. A
large-scale empirical evaluation we performed across more than 20k apps shows
encouraging preliminary results, but also highlights future challenges to
overcome.
