Trustworthy AI Software Engineers

2026-06-04 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Advanced, long

Summary

A vision paper re-examines the definition of an AI software engineer and critically analyzes what makes such an agent trustworthy, moving beyond mere coding capabilities. Grounded in established software engineering (SE) definitions from IEEE, ACM, and SWEBoK, the paper conceptualizes AI software engineers as participants in human-AI SE teams, distinguishing trustworthiness as a system property rather than a subjective human attitude. It identifies key dimensions for trustworthiness, including technical quality, transparency, accountability, epistemic humility, and societal/ethical alignment. The analysis highlights a fundamental trust measurement gap, noting that not all critical aspects of trust are easily quantifiable. The paper outlines implications for the design, evaluation, and governance of AI SE systems, advocating for an ethics-by-design approach to foster appropriate trust in future human-AI SE teams. This framework addresses concerns about current LLM-based agents like Claude Code, Codex, Kiro, AutoCodeRover, SWE-agent, and RepairAgent, which, despite productivity gains, face skepticism due to issues like hallucination and misunderstanding vulnerabilities.

Key takeaway

For AI scientists and software engineering leaders developing AI coding agents, you must shift focus from mere productivity to comprehensive trustworthiness. Your systems should be designed as collaborative team members, not replacements, capable of handling socio-technical tasks beyond code generation. Prioritize ethics-by-design, ensuring your AI agents demonstrate transparency, accountability, and epistemic humility to build justified reliance and mitigate risks of misplaced trust in human-AI SE teams.

Key insights

Trustworthy AI software engineers must integrate into human-AI teams, extending beyond coding to socio-technical tasks and ethical alignment.

Principles

• AI SEs must handle tasks beyond coding.
• Trustworthiness is a system property, not subjective.
• Ethics-by-design is crucial for AI SE systems.

Method

The paper defines an AI software engineer as an agent that accepts SE tasks, plans multi-step actions with tools, reasons over context, produces/validates artifacts, and collaborates ethically with humans.

In practice

• Design AI agents for team collaboration.
• Implement ethics-by-design in AI SE systems.

Topics

• AI Software Engineering
• Trustworthy AI
• AI Agents
• Human-AI Collaboration
• Ethics-by-Design
• Software Development Lifecycle

Code references

• kirodotdev/Kiro
• openai/codex

Best for: Research Scientist, AI Scientist, Software Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.