Lunch at 12:30pm, talk at 1pm, in 148 Fitzpatrick
Title: Context-Aware Models for Automatic Source Code Summarization
Abstract: Source Code Summarization is the task of writing natural language descriptions of source code. The state-of-the-art for automatic source code summarization are neural networks developed for machine translation. These are usually designed to accept a snippet of source code as a sequence of tokens and generate a description, patterned on sequence-to-sequence learning. However, often the information required to summarize the subroutine is not inside the subroutine. The necessary information lives in the “context” around the code, such as other subroutines, files, and build files. In this talk, I will present my research on context-aware neural models for better automatic source code summarization. I will discuss the intuition behind each type of context we encode, as well as present our techniques and results.
Bio: Aakash Bansal is a fifth-year Ph.D. student at the University of Notre Dame, advised by Collin McMillan. His research focuses on the application of NLP research to advance code intelligence tasks, particularly automatic source code summarization. Before Notre Dame, he received his master’s degree in machine learning from the University of Surrey and bachelor’s degree in computer systems engineering from the University of Lancaster.