Zhe-Ying Zhang

An Autonomy Aware Metamodel for Human AI Collaboration in Software Engineering

Artificial Intelligence (AI) is shifting software engineering from tool-supported processes towards AI-first collaboration, where authority is dynamically distributed across human and artificial actors. However, existing method engineering approaches assume static, human-centric control and provide limited support explicitly capturing evolving autonomy. This paper presents a vision for autonomy-aware method engineering by proposing a metamodel that treats autonomy not as a fixed property of an actor, but as a derived, situation-dependent authority assignment determined by task, context, and collaboration pattern. The metamodel formalizes autonomy through four authority dimensions: task execution, task decomposition, task initiation, and collaboration reconfiguration. Through an analytical instantiation with a multi-agent requirements analysis tool, we illustrate how the metamodel supports dynamic authority assignment. This work provides a conceptual foundation for governance-aware, adaptable, and AI-first software engineering methods.

Integrating AI into Requirements Quality Learning in Software Engineering Education: A TPACK-Guided Empirical Study

TPACK-guided scaffolding can align AI affordances with pedagogical goals and RE content, offering design guidance for responsible AI integration in RE education.

A Tool for Test Case Scenarios Generation Using Large Language Models

A web-based software tool is introduced that employs an LLM-based agent and prompt engineering to automate the generation of test case scenarios against user requirements and crafting test case scenarios based on these stories.

Prioritizing Software Requirements Using Large Language Models

A web-based software tool utilizing AI agents and prompt engineering to automate task prioritization and apply diverse prioritization techniques, aimed at enhancing project management within the agile framework is introduced.

AI based Multiagent Approach for Requirements Elicitation and Analysis

Results corroborate the effectiveness of LLMs in improving and streamlining RE phases by analyzing the semantic similarity and API performance of different models, as well as their effectiveness and efficiency in requirements analysis.

From Specification to Service: Accelerating API-First Development Using Multi-Agent Systems

The results indicate that if the OpenAPI specification is kept small and focused, LLM-based multi-agent systems are capable of generating complete functional code with business logic that aligns to the specification.

LLM-Generated Microservice Implementations from RESTful API Definitions

A system that uses Large Language Models (LLMs) to automate the API-first development of RESTful microservices and assists in creating OpenAPI specification, generating server code from it, and refining the code through a feedback loop that analyzes execution logs and error messages is presented.

Enhancing Regulation-Adherent Requirement Engineering with Contextual AI: An Industrial Study

Autonomous Legacy Web Application Upgrades Using a Multi-Agent System

An LLM-based multi-agent system that autonomously upgrades legacy web applications to the latest versions and maintains context across tasks and agents, improving solution quality over the base model in some cases is proposed.

A Multi-agent LLM System for Automated Requirements Analysis: A Study on User Story Generation and Prioritization

A Context-Aware Multi-agent Approach to Enhancing User Story Management in Agile Software Development

Epic-Organized vs. Requirement-Aligned Gherkin: An Empirical Evaluation of LLM-Based Acceptance Criteria Generation

Overall, the results suggest that epic-organized generation can improve perceived Gherkin quality while maintaining comparable semantic coverage, although broader replication is needed before generalizing this finding.