Autofolding for Source Code Summarization
This addresses the challenge of program comprehension for developers by making code folding more practical and usable, though it is incremental as it builds on existing code folding features.
The paper tackles the problem of automatically summarizing source code by folding less informative regions, introducing the autofolding problem and showing that their method yields a 28% error reduction compared to simpler baselines.
Developers spend much of their time reading and browsing source code, raising new opportunities for summarization methods. Indeed, modern code editors provide code folding, which allows one to selectively hide blocks of code. However this is impractical to use as folding decisions must be made manually or based on simple rules. We introduce the autofolding problem, which is to automatically create a code summary by folding less informative code regions. We present a novel solution by formulating the problem as a sequence of AST folding decisions, leveraging a scoped topic model for code tokens. On an annotated set of popular open source projects, we show that our summarizer outperforms simpler baselines, yielding a 28% error reduction. Furthermore, we find through a case study that our summarizer is strongly preferred by experienced developers. More broadly, we hope this work will aid program comprehension by turning code folding into a usable and valuable tool.