Human-In-The-Loop Software Development Agents: Challenges and Future Directions
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 5 of its 6 references that are in the map. First posted on arXiv 25 Apr 2025.
Builds on
5 of 6 references.
Closest ancestors · 1
Same direction, newest first
Other references · 4
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2022ReCode: Robustness Evaluation of Code Generation Modelscited by 103
- 2024CodeJudge: Evaluating Code Generation with Large Language Modelscited by 44
- 2023Automating the Correctness Assessment of AI-generated Code for Security Contextscited by 6
Built on by
Citing papers per year: 2026: 3.
3 papers.
- 2026LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Reviewcited by 3
- 2026Governed AI-Assisted Engineering: Graduated Human Oversight for Agentic Code Generation in Regulated Domainscited by 2
- 2026Towards Reliable ML Feature Engineering via Planning in Constrained-Topology of LLM Agentscited by 1
- 2026Governed AI-Assisted Engineering: Graduated Human Oversight for Agentic Code Generation in Regulated Domains21 Jun 2026
- 2026LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review25 Feb 2026
- 2026Towards Reliable ML Feature Engineering via Planning in Constrained-Topology of LLM Agents15 Jan 2026
Similar, not cited
Close in wording, no citation either way
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- 2026Human-Agent versus Human Pull Requests: A Testing-Focused Characterization and Comparison
- 2025A Survey on Code Generation with LLM-based Agents
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