59 citations · 145 across the 33 of their papers we have counts for
33 papers
Chiseling Out Efficiency: Structured Skeleton Supervision for Efficient Code Generation
Yu Yu, Zhihong Sun, Jia Li +8
Large Language Models (LLMs) are capable of generating syntactically correct and functionally complete programs, greatly streamlining software development. However, recent studies…
ClarifySTL: An Interactive LLM Agent Framework for STL Transformation through Requirements Clarification
Yue Fang, Zhi Jin, Jie An +3
Signal Temporal Logic (STL) is a formal language for specifying real-time behaviors of cyber-physical systems (CPS). Automatically transforming natural language requirements into S…
Bridging the Gap between User Intent and LLM: A Requirement Alignment Approach for Code Generation
Jia Li, Ruiqi Bai, Yangkang Luo +7
Code generation refers to automatically producing executable programs from user requirements. Recently, researchers have explored approaches to enhance the correctness of generated…
A Needle in a Haystack: Intent-driven Reusable Artifacts Recommendation with LLMs
Dongming Jin, Zhi Jin, Xiaohong Chen +6
In open source software development, the reuse of existing artifacts has been widely adopted to avoid redundant implementation work. Reusable artifacts are considered more efficien…
A Viable Paradigm of Software Automation: Iterative End-to-End Automated Software Development
Jia Li, Zhi Jin, Huangzhao Zhang +3
Software development automation is a long-term goal in software engineering. With the development of artificial intelligence (AI), more and more researchers are exploring approache…
AdapTrack: Constrained Decoding without Distorting LLM's Output Intent
Yongmin Li, Jia Li, Ge Li +1
Language model-based code generation and completion tools have been widely adopted, but they may sometimes produce code that does not meet necessary constraints, such as syntactic…
Ensembling Large Language Models for Code Vulnerability Detection: An Empirical Evaluation
Zhihong Sun, Jia Li, Yao Wan +7
Code vulnerability detection is crucial for ensuring the security and reliability of modern software systems. Recently, Large Language Models (LLMs) have shown promising capabiliti…
A System Model Generation Benchmark from Natural Language Requirements
Dongming Jin, Zhi Jin, Linyu Li +3
System models, a critical artifact in software development, provide a formal abstraction of both the structural and behavioral aspects of software systems, which can facilitate the…
GraphCodeAgent: Dual Graph-Guided LLM Agent for Retrieval-Augmented Repo-Level Code Generation
Jia Li, Xianjie Shi, Kechi Zhang +10
Writing code requires significant time and effort in software development. To automate this process, researchers have made substantial progress for code generation. Recently, large…
PROPHET: An Inferable Future Forecasting Benchmark with Causal Intervened Likelihood Estimation
Zhengwei Tao, Pu Wu, Zhi Jin +8
Predicting future events based on news on the Web stands as one of the ultimate aspirations of artificial intelligence. Recent advances in large language model (LLM)-based systems…
SEAlign: Alignment Training for Software Engineering Agent
Kechi Zhang, Huangzhao Zhang, Ge Li +4
Recent advances in code generation models have demonstrated impressive capabilities in automating software development tasks, yet these models still struggle in real-world software…
LONGCODEU: Benchmarking Long-Context Language Models on Long Code Understanding
Jia Li, Xuyuan Guo, Lei Li +7
Current advanced long-context language models offer great potential for real-world software engineering applications. However, progress in this critical domain remains hampered by…
FastCoder: Accelerating Repository-level Code Generation via Efficient Retrieval and Verification
Qianhui Zhao, Li Zhang, Fang Liu +6
Code generation is a latency-sensitive task that demands high timeliness. However, with the growing interest and inherent difficulty in repository-level code generation, most exist…
Focused-DPO: Enhancing Code Generation Through Focused Preference Optimization on Error-Prone Points
Kechi Zhang, Ge Li, Jia Li +2
Code generation models have shown significant potential for automating programming tasks. However, the challenge of generating accurate and reliable code persists due to the highly…
Generating Equivalent Representations of Code By A Self-Reflection Approach
Jia Li, Ge Li, Lecheng Wang +2
Equivalent Representations (ERs) of code are textual representations that preserve the same semantics as the code itself, e.g., natural language comments and pseudocode. ERs play a…
Showing LLM-Generated Code Selectively Based on Confidence of LLMs
Jia Li, Yuqi Zhu, Yongmin Li +2
Large Language Models (LLMs) have shown impressive abilities in code generation, but they may generate erroneous programs. Reading a program takes ten times longer than writing it.…
Sifting through the Chaff: On Utilizing Execution Feedback for Ranking the Generated Code Candidates
Zhihong Sun, Yao Wan, Jia Li +4
Large Language Models (LLMs), such as GPT-4, StarCoder, and CodeLlama, are transforming the way developers approach programming by automatically generating code based on given natu…
M2CVD: Enhancing Vulnerability Semantic through Multi-Model Collaboration for Code Vulnerability Detection
Ziliang Wang, Ge Li, Jia Li +3
Large Language Models (LLMs) have strong capabilities in code comprehension, but fine-tuning costs and semantic alignment issues limit their project-specific optimization; converse…
Line-level Semantic Structure Learning for Code Vulnerability Detection
Ziliang Wang, Ge Li, Jia Li +3
Unlike the flow structure of natural languages, programming languages have an inherent rigidity in structure and grammar.However, existing detection methods based on pre-trained mo…
Exploring and Unleashing the Power of Large Language Models in Automated Code Translation
Zhen Yang, Fang Liu, Zhongxing Yu +7
Code translation tools (transpilers) are developed for automatic source-to-source translation. Although learning-based transpilers have shown impressive enhancement against rule-ba…
A Comprehensive Evaluation on Event Reasoning of Large Language Models
Zhengwei Tao, Zhi Jin, Yifan Zhang +7
Event reasoning is a fundamental ability that underlies many applications. It requires event schema knowledge to perform global reasoning and needs to deal with the diversity of th…
CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges
Kechi Zhang, Jia Li, Ge Li +2
Large Language Models (LLMs) have shown promise in automated code generation but typically excel only in simpler tasks such as generating standalone code units. Real-world software…
DevEval: Evaluating Code Generation in Practical Software Projects
Jia Li, Ge Li, Yunfei Zhao +14
How to evaluate Large Language Models (LLMs) in code generation is an open question. Many benchmarks have been proposed but are inconsistent with practical software projects, e.g.,…
ChatCoder: Chat-based Refine Requirement Improves LLMs' Code Generation
Zejun Wang, Jia Li, Ge Li +1
Large language models have shown good performances in generating code to meet human requirements. However, human requirements expressed in natural languages can be vague, incomplet…