163 citations · 302 across the 32 of their papers we have counts for
34 papers
Trustworthy Runtime Error Healing in Real-World Repositories: A Benchmark and Guardrail
Gou Tan, Pengfei Chen, Zhensu Sun +9
Runtime error healing lets a crashed program continue by generating code that repairs its live runtime state. Recent work shows that LLMs can generate such healing code, but it is…
Learning from the Test: Self-Referential Differential Testing for Deep RL Agents
Junda He, Jieke Shi, Zhou Yang +2
Deep Reinforcement Learning (DRL) has achieved significant success in complex decision-making problems. As DRL systems are increasingly deployed in real-world applications, ensurin…
Beyond Text Matching: Towards Reference-Free Evaluation for Human-Oriented Binary Reverse Engineering
Xiuwei Shang, Li Hu, Xiao Jiang +7
Human-Oriented Binary Reverse Engineering (HOBRE) aims to transform decompiled pseudocode into a more human-friendly representation, thereby reducing the cognitive burden of revers…
AgentChaos: Chaos Engineering for Agent Systems via Programmatic Fault Injection
Gou Tan, Zhensu Sun, Jieke Shi +10
Agent systems rely on LLM APIs for every response, but these APIs can return server errors, truncated responses, or corrupted content that propagates through downstream agents and…
Fail-Fast, Restart-Smart: Early Failure Prediction and Restart for SWE Agentic Tasks
Chenyu Wang, Yunbo Lyu, Junda He +4
Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates. Failed runs tend to be longer a…
Lossless Tensor Compression as Program Synthesis
Jieke Shi, Junda He, Wenjia Jiang +11
Model checkpoints are growing in both number and size, which makes archival, transfer, and deployment increasingly costly. General-purpose compressors can reduce storage requiremen…
AgentSZZ: Teaching the LLM Agent to Play Detective with Bug-Inducing Commits
Yunbo Lyu, Jieke Shi, Hong Jin Kang +8
The SZZ algorithm is the dominant technique for identifying bug-inducing commits and underpins many software engineering tasks, such as defect prediction and vulnerability analysis…
Compiling Code LLMs into Lightweight Executables
Jieke Shi, Junda He, Zhou Yang +6
The demand for better prediction accuracy and higher execution performance in neural networks continues to grow. The emergence and success of Large Language Models (LLMs) have prod…
Beyond Function-Level Analysis: Context-Aware Reasoning for Inter-Procedural Vulnerability Detection
Yikun Li, Ting Zhang, Jieke Shi +10
Recent progress in ML and LLMs has improved vulnerability detection, and recent datasets have reduced label noise and unrelated code changes. However, most existing approaches stil…
LLM-as-a-Judge for Software Engineering: Literature Review, Vision, and the Road Ahead
Junda He, Jieke Shi, Terry Yue Zhuo +5
The rapid integration of Large Language Models (LLMs) into software engineering (SE) has revolutionized tasks like code generation, producing a massive volume of software artifacts…
Curiosity-Driven Testing for Sequential Decision-Making Process
Junda He, Zhou Yang, Jieke Shi +5
Sequential decision-making processes (SDPs) are fundamental for complex real-world challenges, such as autonomous driving, robotic control, and traffic management. While recent adv…
Assessing and Advancing Benchmarks for Evaluating Large Language Models in Software Engineering Tasks
Xing Hu, Feifei Niu, Junkai Chen +5
Large language models (LLMs) are gaining increasing popularity in software engineering (SE) due to their unprecedented performance across various applications. These models are inc…
Artificial Intelligence for Software Architecture: Literature Review and the Road Ahead
Alessio Bucaioni, Martin Weyssow, Junda He +2
Artificial intelligence is increasingly applied across software engineering, yet its explicit role in software architecture remains insufficiently understood. Architectural practic…
From Code to Courtroom: LLMs as the New Software Judges
Junda He, Jieke Shi, Terry Yue Zhuo +5
Recently, Large Language Models (LLMs) have been increasingly used to automate SE tasks such as code generation and summarization. However, evaluating the quality of LLM-generated…
Identifying and Mitigating API Misuse in Large Language Models
Terry Yue Zhuo, Junda He, Jiamou Sun +4
API misuse in code generated by large language models (LLMs) presents a serious and growing challenge in software development, as although LLMs demonstrate impressive code generati…
LessLeak-Bench: A First Investigation of Data Leakage in LLMs Across 83 Software Engineering Benchmarks
Xin Zhou, Martin Weyssow, Ratnadira Widyasari +7
Large Language Models (LLMs) are widely utilized in software engineering (SE) tasks, such as code generation and automated program repair. However, their reliance on extensive and…
A Functional Software Reference Architecture for LLM-Integrated Systems
Alessio Bucaioni, Martin Weyssow, Junda He +2
The integration of large language models into software systems is transforming capabilities such as natural language understanding, decision-making, and autonomous task execution.…
Synthesizing Efficient and Permissive Programmatic Runtime Shields for Neural Policies
Jieke Shi, Junda He, Zhou Yang +2
With the increasing use of neural policies in control systems, ensuring their safety and reliability has become a critical software engineering task. One prevalent approach to ensu…
Finding Safety Violations of AI-Enabled Control Systems through the Lens of Synthesized Proxy Programs
Jieke Shi, Zhou Yang, Junda He +4
Given the increasing adoption of modern AI-enabled control systems, ensuring their safety and reliability has become a critical task in software testing. One prevalent approach to…
PTM4Tag+: Tag Recommendation of Stack Overflow Posts with Pre-trained Models
Junda He, Bowen Xu, Zhou Yang +5
Stack Overflow is one of the most influential Software Question & Answer (SQA) websites, hosting millions of programming-related questions and answers. Tags play a critical role in…
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
Terry Yue Zhuo, Minh Chien Vu, Jenny Chim +30
Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to…
LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead
Junda He, Christoph Treude, David Lo
Integrating Large Language Models (LLMs) into autonomous agents marks a significant shift in the research landscape by offering cognitive abilities that are competitive with human…
APIDocBooster: An Extract-Then-Abstract Framework Leveraging Large Language Models for Augmenting API Documentation
Chengran Yang, Jiakun Liu, Bowen Xu +5
API documentation is often the most trusted resource for programming. Many approaches have been proposed to augment API documentation by summarizing complementary information from…
CCBERT: Self-Supervised Code Change Representation Learning
Xin Zhou, Bowen Xu, DongGyun Han +3
Numerous code changes are made by developers in their daily work, and a superior representation of code changes is desired for effective code change analysis. Recently, Hoang et al…