Neural Code Summarization
This addresses the tedious task of manual code description updates for software developers, but appears incremental as it builds on existing neural methods for code summarization.
The paper tackles the problem of automatically generating readable summaries for software code to aid program comprehension, proposing a neural approach that is evaluated on benchmarked and custom datasets with comparisons between original and generated results.
Code summarization is the task of generating readable summaries that are semantically meaningful and can accurately describe the presumed task of a software. Program comprehension has become one of the most tedious tasks for knowledge transfer. As the codebase evolves over time, the description needs to be manually updated each time with the changes made. An automatic approach is proposed to infer such captions based on benchmarked and custom datasets with comparison between the original and generated results.