activity
20242026
most citedMARE: Multi-Agents Collaboration Framework for Requirements Engineering

7 citations · 8 across the 9 of their papers we have counts for

collaborators

12 papers

cs.SE2026

From Chat to Interview: Agentic Requirements Elicitation with an Experience Ontology

Dongming Jin, Zhi Jin, Yaotian Yang +5

Requirements elicitation interviews are crucial and time-consuming in requirements engineering, but heavily rely on the experience of requirements analysts. Although recent advance…

cs.SE2026

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…

cs.SE2026

ReqElicitGym: An Evaluation Environment for Interview Competence in Conversational Requirements Elicitation

Dongming Jin, Zhi Jin, Zheng Fang +4

With the rapid improvement of LLMs' coding capabilities, the bottleneck of LLM-based automated software development is shifting from generating correct code to eliciting users' req…

cs.SE2025

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…

cs.FL2025

RESTL: Reinforcement Learning Guided by Multi-Aspect Rewards for Signal Temporal Logic Transformation

Yue Fang, Jin Zhi, Jie An +3

Signal Temporal Logic (STL) is a powerful formal language for specifying real-time specifications of Cyber-Physical Systems (CPS). Transforming specifications written in natural la…

cs.SE2025

Automated Concern Extraction from Textual Requirements of Cyber-Physical Systems: A Multi-solution Study

Dongming Jin, Zhi Jin, Xiaohong Chen +5

Cyber-physical systems (CPSs) are characterized by a deep integration of the information space and the physical world, which makes the extraction of requirements concerns more chal…

cs.SE2025

UserTrace: User-Level Requirements Generation and Traceability Recovery from Software Project Repositories

Dongming Jin, Zhi Jin, Yiran Zhang +5

Software maintainability critically depends on high-quality requirements descriptions and explicit traceability between requirements and code. Although automated code summarization…

cs.SE2025

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…

cs.SE2025

A Conceptual Framework for Requirements Engineering of Pretrained-Model-Enabled Systems

Dongming Jin, Zhi Jin, Linyu Li +1

Recent advances in large pretrained models have led to their widespread integration as core components in modern software systems. The trend is expected to continue in the foreseea…

cs.CL2025

Enhancing Transformation from Natural Language to Signal Temporal Logic Using LLMs with Diverse External Knowledge

Yue Fang, Zhi Jin, Jie An +3

Temporal Logic (TL), especially Signal Temporal Logic (STL), enables precise formal specification, making it widely used in cyber-physical systems such as autonomous driving and ro…

cs.SE2024★ 1 cited

An Evaluation of Requirements Modeling for Cyber-Physical Systems via LLMs

Dongming Jin, Shengxin Zhao, Zhi Jin +4

Cyber-physical systems (CPSs) integrate cyber and physical components and enable them to interact with each other to meet user needs. The needs for CPSs span rich application domai…

cs.SE2024★ 7 cited

MARE: Multi-Agents Collaboration Framework for Requirements Engineering

Dongming Jin, Zhi Jin, Xiaohong Chen +1

Requirements Engineering (RE) is a critical phase in the software development process that generates requirements specifications from stakeholders' needs. Recently, deep learning t…