Core Concepts
EyeTrans integrates human attention with machine attention in Transformer models to enhance neural code summarization, demonstrating significant performance improvements.
Abstract
EyeTrans introduces a method to combine human and machine attention in Transformer models for code summarization. By analyzing eye-tracking data and incorporating it into the model, EyeTrans shows substantial enhancements in functional and general code summarization tasks. The study reveals structured patterns in how programmers read code, highlighting the potential benefits of integrating human attention into AI models for software engineering.
The content discusses the development of EyeTrans, a method that combines human and machine attention in Transformer models for neural code summarization. By analyzing eye-tracking data from programmers reading code snippets, the study demonstrates significant improvements in both functional and general code summarization tasks. The research also uncovers structured patterns in how programmers focus on different parts of the code, indicating the potential advantages of integrating human attention into AI models for software engineering.
Stats
Integrating human attention leads to an improvement of up to 29.91% in Functional Summarization and up to 6.39% in General Code Summarization performance.