Overwatch: Learning Patterns in Code Edit Sequences

TL;DR

Overwatch is a novel technique that learns patterns in developers' code edit sequences using temporal context, significantly improving IDE suggestions by predicting common and new edits with high precision.

Contribution

This paper introduces Overwatch, a new method for learning code edit sequence patterns from IDE usage data, enhancing code suggestion accuracy and coverage.

Findings

Overwatch achieves 78% precision in predicting code edits.

It can predict edits that lack existing IDE support.

Overwatch improves developer assistance by capturing temporal editing patterns.

Abstract

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.