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Found 7 papers, 4 papers with code
Revolutionizing API Documentation through Summarization
This study tackles the challenges associated with interpreting Application Programming Interface (API) documentation, an integral aspect of software development.
Leveraging Deep Learning for Abstractive Code Summarization of Unofficial Documentation
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.
Enhancing API Documentation through BERTopic Modeling and Summarization
As the amount of textual data in various fields, including software development, continues to grow, there is a pressing demand for efficient and effective extraction and presentation of meaningful insights.
Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Study
Our research investigates the recommendation of code examples to aid software developers, a practice that saves developers significant time by providing ready-to-use code snippets.
Leveraging Data Mining Algorithms to Recommend Source Code Changes
Objectives: This paper proposes an automatic method for recommending source code changes using four data mining algorithms.
Towards Code Summarization of APIs Based on Unofficial Documentation Using NLP Techniques
Each programming language comes with official documentation to guide developers with APIs, methods, and classes.
Leveraging Unsupervised Learning to Summarize APIs Discussed in Stack Overflow
Automated source code summarization is a task that generates summarized information about the purpose, usage, and--or implementation of methods and classes to support understanding of these code entities.
