3 research outputs found
Leveraging Deep Learning for Abstractive Code Summarization of Unofficial Documentation
Usually, programming languages have official documentation to guide
developers with APIs, methods, and classes. However, researchers identified
insufficient or inadequate documentation examples and flaws with the API's
complex structure as barriers to learning an API. As a result, developers may
consult other sources (StackOverflow, GitHub, etc.) to learn more about an API.
Recent research studies have shown that unofficial documentation is a valuable
source of information for generating code summaries. We, therefore, have been
motivated to leverage such a type of documentation along with deep learning
techniques towards generating high-quality summaries for APIs discussed in
informal documentation. This paper proposes an automatic approach using the
BART algorithm, a state-of-the-art transformer model, to generate summaries for
APIs discussed in StackOverflow. We built an oracle of human-generated
summaries to evaluate our approach against it using ROUGE and BLEU metrics
which are the most widely used evaluation metrics in text summarization.
Furthermore, we evaluated our summaries empirically against a previous work in
terms of quality. Our findings demonstrate that using deep learning algorithms
can improve summaries' quality and outperform the previous work by an average
of %57 for Precision, %66 for Recall, and %61 for F-measure, and it runs 4.4
times faster