Human-In-The-Loop Software Development Agents: Challenges and Future Directions

Jirat Pasuksmit; Wannita Takerngsaksiri; Patanamon Thongtanunam; Chakkrit Tantithamthavorn; Ruixiong Zhang; Shiyan Wang; Fan Jiang; Jing Li; Evan Cook; Kun Chen; Ming Wu

arXiv · 2506.11009

Human-In-The-Loop Software Development Agents: Challenges and Future Directions

Jirat Pasuksmit·Wannita Takerngsaksiri·Patanamon Thongtanunam·Chakkrit Tantithamthavorn·Ruixiong Zhang·Shiyan Wang·Fan Jiang·Jing Li·Evan Cook·Kun Chen·Ming Wu

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Abstract

Multi-agent LLM-driven systems for software development are rapidly gaining traction, offering new opportunities to enhance productivity. At Atlassian, we deployed Human-in-the-Loop Software Development Agents to resolve Jira work items and evaluated the generated code quality using functional correctness testing and GPT-based similarity scoring. This paper highlights two major challenges: the high computational costs of unit testing and the variability in LLM-based evaluations. We also propose future research directions to improve evaluation frameworks for Human-In-The-Loop software development tools.

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Jirat Pasuksmit, Wannita Takerngsaksiri, Patanamon Thongtanunam, Chakkrit Tantithamthavorn, Ruixiong Zhang, Shiyan Wang, Fan Jiang, Jing Li, Evan Cook, Kun Chen, Ming Wu. 2025-04-25. Human-In-The-Loop Software Development Agents: Challenges and Future Directions. https://arxiv.org/abs/2506.11009

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