Empowering Agile-Based Generative Software Development through Human-AI Teamwork
Sai Zhang, Zhenchang Xing, Ronghui Guo, Fangzhou Xu, Lei Chen,, Zhaoyuan Zhang, Xiaowang Zhang, Zhiyong Feng, Zhiqiang Zhuang

TL;DR
AgileGen introduces an interactive human-AI teamwork approach for generative software development, using testable requirements and scenario memory to improve semantic consistency, user participation, and overall system performance.
Contribution
It pioneers the integration of testable requirements with human-AI collaboration in agile software development, enhancing semantic accuracy and user involvement.
Findings
AgileGen outperforms existing methods by 16.4%.
Higher user satisfaction achieved with AgileGen.
Memory pool improves scenario reliability.
Abstract
In software development, the raw requirements proposed by users are frequently incomplete, which impedes the complete implementation of application functionalities. With the emergence of large language models, recent methods with the top-down waterfall model employ a questioning approach for requirement completion, attempting to explore further user requirements. However, users, constrained by their domain knowledge, lack effective acceptance criteria, which fail to capture the implicit needs of the user. Moreover, the cumulative errors of the waterfall model can lead to discrepancies between the generated code and user requirements. The Agile methodologies reduce cumulative errors through lightweight iteration and collaboration with users, but the challenge lies in ensuring semantic consistency between user requirements and the code generated. We propose AgileGen, an agile-based…
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Taxonomy
TopicsSoftware Engineering Techniques and Practices
