# Human-AI Collaboration for Scaling Agile Regression Testing: An Agentic-AI Teammate from Manual to Automated Testing

arXiv: https://arxiv.org/abs/2603.08190 · HTML: https://mlsanity.com/paper/2603.08190/

## Key facts

- Cited by 1 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 3 of its 13 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 3 of 3)

- [ALMAS: an Autonomous LLM-based Multi-Agent Software Engineering Framework](https://mlsanity.com/paper/2510.03463/) (2025)
- [Large Language Models for Software Testing: A Research Roadmap](https://mlsanity.com/paper/2509.25043/) (2025)
- [Between Policy and Practice: GenAI Adoption in Agile Software Development Teams](https://mlsanity.com/paper/2601.07051/) (2026)

## Built on by (most cited first, 1 of 1)

- [Describing Agentic AI Systems with C4: Lessons from Industry Projects](https://mlsanity.com/paper/2603.15021/) (2026)

## Abstract (arXiv)

Automated regression testing is essential for maintaining rapid, high-quality delivery in Agile and Scrum organizations. Many teams, including Hacon (a Siemens company), face a persistent gap: validated test specifications accumulate faster than they are automated, limiting regression coverage and increasing manual work. This paper reports an exploratory industrial case study of the Hacon Test Automation Copilot, an agentic AI system that generates system-level regression test scripts from validated specifications using retrieval-augmented generation and a multi-agent workflow. Integrated with Hacon's CI pipelines, the Copilot operates asynchronously as a "silent AI teammate", producing candidate scripts for human review. Mixed-method evaluation shows the AI accelerates script authoring and increases throughput, with 30-50% code reuse. However, human review remains necessary for maintainability and correct domain interpretation. Clear specifications, explicit governance, and ongoing human-AI collaboration are critical. We conclude with lessons for scaling regression automation and enabling effective human-AI teaming in Agile settings.