Overwatch: Learning Patterns in Code Edit Sequences.
Integrated Development Environments (IDEs) provide tool support to automate
many source code editing tasks. Traditionally, IDEs use only the spatial
context, i.e., the location where the developer is editing, to generate
candidate edit recommendations. However, spatial context alone is often not
sufficient to confidently predict the developer’s next edit, and thus IDEs
generate many suggestions at a location. Therefore, IDEs generally do not
actively offer suggestions and instead, the developer is usually required to
click on a specific icon or menu and then select from a large list of potential
suggestions. As a consequence, developers often miss the opportunity to use the
tool support because they are not aware it exists or forget to use it.
To better understand common patterns in developer behavior and produce better
edit recommendations, we can additionally use the temporal context, i.e., the
edits that a developer was recently performing. To enable edit recommendations
based on temporal context, we present Overwatch, a novel technique for learning
edit sequence patterns from traces of developers’ edits performed in an IDE.
Our experiments show that Overwatch has 78% precision and that Overwatch not
only completed edits when developers missed the opportunity to use the IDE tool
support but also predicted new edits that have no tool support in the IDE.
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