Human-AI Collaboration for Scaling Agile Regression Testing: An Agentic-AI Teammate from Manual to Automated Testing
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910124286672896 |
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| author | Outmani, Moustapha El Shenoy, Manthan Venkataramana Hatahet, Ahmad Rausch, Andreas Kniep, Tim Niklas Raddatz, Thomas King, Benjamin |
| author_facet | Outmani, Moustapha El Shenoy, Manthan Venkataramana Hatahet, Ahmad Rausch, Andreas Kniep, Tim Niklas Raddatz, Thomas King, Benjamin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08190 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Human-AI Collaboration for Scaling Agile Regression Testing: An Agentic-AI Teammate from Manual to Automated Testing Outmani, Moustapha El Shenoy, Manthan Venkataramana Hatahet, Ahmad Rausch, Andreas Kniep, Tim Niklas Raddatz, Thomas King, Benjamin Software Engineering 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. |
| title | Human-AI Collaboration for Scaling Agile Regression Testing: An Agentic-AI Teammate from Manual to Automated Testing |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2603.08190 |