59 citations · 124 across the 7 of their papers we have counts for
7 papers
Reasoning Runtime Behavior of a Program with LLM: How Far Are We?
Junkai Chen, Zhiyuan Pan, Xing Hu +3
Large language models for code (i.e., code LLMs) have shown strong code understanding and generation capabilities. To evaluate the capabilities of code LLMs in various aspects, man…
EditSum: A Retrieve-and-Edit Framework for Source Code Summarization
Jia Li, Yongmin Li, Ge Li +3
Existing studies show that code summaries help developers understand and maintain source code. Unfortunately, these summaries are often missing or outdated in software projects. Co…
Are They All Good? Studying Practitioners' Expectations on the Readability of Log Messages
Zhenhao Li, An Ran Chen, Xing Hu +3
Developers write logging statements to generate logs that provide run-time information for various tasks. The readability of log messages in the logging statements (i.e., the descr…
SkCoder: A Sketch-based Approach for Automatic Code Generation
Jia Li, Yongmin Li, Ge Li +3
Recently, deep learning techniques have shown great success in automatic code generation. Inspired by the code reuse, some researchers propose copy-based approaches that can copy t…
Poison Attack and Defense on Deep Source Code Processing Models
Jia Li, Zhuo Li, Huangzhao Zhang +4
In the software engineering community, deep learning (DL) has recently been applied to many source code processing tasks. Due to the poor interpretability of DL models, their secur…
CodeEditor: Learning to Edit Source Code with Pre-trained Models
Jia Li, Ge Li, Zhuo Li +4
Developers often perform repetitive code editing activities for various reasons (e.g., code refactoring) during software development. Pre-trained code editing models have achieved…