109 citations · 218 across the 19 of their papers we have counts for
18 papers · 1 filter
In-Context Code-Text Learning for Bimodal Software Engineering
Xunzhu Tang, Liran Wang, Yonghui Liu +6
Bimodal software analysis initially appeared to be within reach with the advent of large language models. Unfortunately, the complex interplay of natural language text and code in…
CREF: An LLM-based Conversational Software Repair Framework for Programming Tutors
Boyang Yang, Haoye Tian, Weiguo Pian +5
Program repair techniques offer cost-saving benefits for debugging within software development and programming education scenarios. With the proven effectiveness of Large Language…
Large-scale, Independent and Comprehensive study of the power of LLMs for test case generation
Wendkûuni C. Ouédraogo, Kader Kaboré, Yinghua Li +5
Unit testing is essential for software reliability, yet manual test creation is time-consuming and often neglected. Search-based software testing improves efficiency but produces t…
MORepair: Teaching LLMs to Repair Code via Multi-Objective Fine-tuning
Boyang Yang, Haoye Tian, Jiadong Ren +5
Within the realm of software engineering, specialized tasks on code, such as program repair, present unique challenges, necessitating fine-tuning Large language models~(LLMs) to un…
CodeAgent: Autonomous Communicative Agents for Code Review
Xunzhu Tang, Kisub Kim, Yewei Song +6
Code review, which aims at ensuring the overall quality and reliability of software, is a cornerstone of software development. Unfortunately, while crucial, Code review is a labor-…
Patch-CLIP: A Patch-Text Pre-Trained Model
Xunzhu Tang, Zhenghan Chen, Saad Ezzini +3
In recent years, patch representation learning has emerged as a necessary research direction for exploiting the capabilities of machine learning in software generation. These repre…