HULA Overview: Human-in-the-Loop Framework and Goals

Apuntes

Human-In-the-Loop Software Development Agents Wannita Takerngsaksiri ∗ , Jirat Pasuksmit † , Patanamon Thongtanunam ‡ , Chakkrit Tantithamthavorn ∗ , Ruixiong Zhang † , Fan Jiang † , Jing Li † , Evan Cook † , Kun Chen † , Ming Wu † ∗ Monash University, Australia. ‡ The University of Melbourne, Australia. † Atlassian, Australia. Abstract—Recently, Large Language Models (LLMs)-based multi-agent paradigms for software engineering are introduced to automatically resolve software development tasks (e.g., from a given issue to source code). However, existing work is evaluated based on historical benchmark datasets, rarely considers human feedback at each stage of the automated software development process, and has not been deployed in practice. In this paper, we introduce a Human-in-the-loop LLM-based Agents framework (HULA) for software development that allows software engineers to refine and guide LLMs when generating coding plans and source code for a given task. We design, implement, and deploy the HULA framework into Atlassian JIRA for internal uses. Through a multi-stage evaluation of the HULA framework, Atlassian software engineers perceive that HULA can minimize the overall devel...