20 citations · 70 across the 16 of their papers we have counts for
19 papers
AI-Mediated Code Comment Improvement
Maria Dhakal, Chia-Yi Su, Robert Wallace +5
This paper describes an approach to improve code comments along different quality axes by rewriting those comments with customized Artificial Intelligence (AI)-based tools. We cond…
Which Code Statements Implement Privacy Behaviors in Android Applications?
Chia-Yi Su, Aakash Bansal, Vijayanta Jain +3
A "privacy behavior" in software is an action where the software uses personal information for a service or a feature, such as a website using location to provide content relevant…
Context-aware Code Summary Generation
Chia-Yi Su, Aakash Bansal, Yu Huang +2
Code summary generation is the task of writing natural language descriptions of a section of source code. Recent advances in Large Language Models (LLMs) and other AI-based technol…
A Study on Developer Behaviors for Validating and Repairing LLM-Generated Code Using Eye Tracking and IDE Actions
Ningzhi Tang, Meng Chen, Zheng Ning +4
The increasing use of large language model (LLM)-powered code generation tools, such as GitHub Copilot, is transforming software engineering practices. This paper investigates how…
Programmer Visual Attention During Context-Aware Code Summarization
Robert Wallace, Aakash Bansal, Zachary Karas +4
Abridged: Programmer attention represents the visual focus of programmers on parts of the source code in pursuit of programming tasks. We conducted an in-depth human study with 10…
EyeTrans: Merging Human and Machine Attention for Neural Code Summarization
Yifan Zhang, Jiliang Li, Zachary Karas +5
Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets. The development of Transformer models has led…
Revisiting File Context for Source Code Summarization
Aakash Bansal, Chia-Yi Su, Collin McMillan
Source code summarization is the task of writing natural language descriptions of source code. A typical use case is generating short summaries of subroutines for use in API docume…
Modeling Programmer Attention as Scanpath Prediction
Aakash Bansal, Chia-Yi Su, Zachary Karas +4
This paper launches a new effort at modeling programmer attention by predicting eye movement scanpaths. Programmer attention refers to what information people intake when performin…
Statement-based Memory for Neural Source Code Summarization
Aakash Bansal, Siyuan Jiang, Sakib Haque +1
Source code summarization is the task of writing natural language descriptions of source code behavior. Code summarization underpins software documentation for programmers. Short d…
Towards Modeling Human Attention from Eye Movements for Neural Source Code Summarization
Aakash Bansal, Bonita Sharif, Collin McMillan
Neural source code summarization is the task of generating natural language descriptions of source code behavior using neural networks. A fundamental component of most neural model…
A Language Model of Java Methods with Train/Test Deduplication
Chia-Yi Su, Aakash Bansal, Vijayanta Jain +2
This tool demonstration presents a research toolkit for a language model of Java source code. The target audience includes researchers studying problems at the granularity level of…
Label Smoothing Improves Neural Source Code Summarization
Sakib Haque, Aakash Bansal, Collin McMillan
Label smoothing is a regularization technique for neural networks. Normally neural models are trained to an output distribution that is a vector with a single 1 for the correct pre…
Semantic Similarity Metrics for Evaluating Source Code Summarization
Sakib Haque, Zachary Eberhart, Aakash Bansal +1
Source code summarization involves creating brief descriptions of source code in natural language. These descriptions are a key component of software documentation such as JavaDocs…
Ensemble Models for Neural Source Code Summarization of Subroutines
Alexander LeClair, Aakash Bansal, Collin McMillan
A source code summary of a subroutine is a brief description of that subroutine. Summaries underpin a majority of documentation consumed by programmers, such as the method summarie…
A Wizard of Oz Study Simulating API Usage Dialogues with a Virtual Assistant
Zachary Eberhart, Aakash Bansal, Collin McMillan
Virtual Assistant technology is rapidly proliferating to improve productivity in a variety of tasks. While several virtual assistants for everyday tasks are well-known (e.g., Siri,…
Project-Level Encoding for Neural Source Code Summarization of Subroutines
Aakash Bansal, Sakib Haque, Collin McMillan
Source code summarization of a subroutine is the task of writing a short, natural language description of that subroutine. The description usually serves in documentation aimed at…
A Neural Question Answering System for Basic Questions about Subroutines
Aakash Bansal, Zachary Eberhart, Lingfei Wu +1
A question answering (QA) system is a type of conversational AI that generates natural language answers to questions posed by human users. QA systems often form the backbone of int…
Action Word Prediction for Neural Source Code Summarization
Sakib Haque, Aakash Bansal, Lingfei Wu +1
Source code summarization is the task of creating short, natural language descriptions of source code. Code summarization is the backbone of much software documentation such as Jav…
The Apiza Corpus: API Usage Dialogues with a Simulated Virtual Assistant
Zachary Eberhart, Aakash Bansal, Collin McMillan
Virtual assistant technology has the potential to make a significant impact in the field of software engineering. However, few SE-related datasets exist that would be suitable for…