NaturalEdit: Code Modification through Direct Interaction with Adaptive Natural Language Representation
This addresses the challenge of code modification for developers by providing a more intuitive and interactive tool, though it appears incremental as it builds on existing natural language summarization approaches.
The paper tackles the problem of cognitively demanding code modification by introducing NaturalEdit, an interactive system that uses adaptive natural language summaries linked to source code, and a user study with 20 developers shows it improves comprehension, intent articulation, and validation while increasing confidence and control.
Code modification requires developers to comprehend code, plan changes, articulate intent, and validate outcomes, making it cognitively demanding. While natural language (NL) code summaries offer a promising external representation of this process, existing approaches remain limited. Systems grounded in exploratory data analysis are restricted to narrow domains, while general-purpose systems enforce fixed NL representations and assume that developers can directly translate vague intent into precise textual edits. We present NaturalEdit, which treats NL code summaries as interactive representations tightly linked to source code. Grounded in the Cognitive Dimensions of Notations, NaturalEdit introduces three key features: (1) adaptive, multi-faceted code summaries with a flexible Abstraction Gradient; (2) interactive mapping mechanisms between summaries and code that ensure tight, structurally stable Closeness of Mapping; and (3) intent-driven bidirectional synchronization that reduces Viscosity during editing while preserving Visibility and Consistency through incremental diffs. A technical evaluation confirms the viability of NaturalEdit, and a user study with 20 developers shows that it improves comprehension, intent articulation, and validation while increasing developers' confidence and sense of control.