[Paper] Trustworthy AI Software Engineers

Source: arXiv - 2602.06310v1

Overview

The paper “Trustworthy AI Software Engineers” re‑thinks what it means for an AI‑driven coding assistant to be called a software engineer and asks how we can make such agents trustworthy partners in development teams. By grounding the discussion in classic software‑engineering definitions and recent work on agentic AI, the authors propose a framework for evaluating and designing AI members of human‑AI SE teams.

Key Contributions

• Conceptual model of AI software engineers as active participants in human‑AI SE teams rather than isolated tools.
• Trustworthiness defined as a system property, not just a user’s feeling, with four concrete dimensions:
  1. Technical quality (correctness, reliability, performance).
  2. Transparency & accountability (explainability, audit trails).
  3. Epistemic humility (recognizing uncertainty and limits).
  4. Societal & ethical alignment (fairness, privacy, compliance).
• Identification of a “trust measurement gap” – many trust‑relevant aspects (e.g., ethical alignment) are hard to quantify with existing metrics.
• Guidelines for ethics‑by‑design in AI‑SE tools, covering design, evaluation, and governance to foster appropriate trust.

Methodology

The authors adopt a vision‑oriented, interdisciplinary approach:

1. Literature synthesis – they map classic software‑engineering standards (e.g., ISO/IEC 12207) to recent AI‑agent research, extracting common responsibilities of a software engineer.
2. Historical analysis – they trace how trust has been treated in SE (from code reviews to formal verification) and extrapolate to AI agents.
3. Dimensional framework building – using the synthesis, they construct the four‑dimensional trust model, each grounded in concrete SE practices (e.g., test coverage for technical quality, model cards for transparency).
4. Gap analysis – they compare the proposed dimensions against existing evaluation tools (benchmark suites, explainability metrics) to highlight what cannot yet be measured reliably.

The methodology is deliberately high‑level, aiming to spark discussion rather than provide empirical validation.

Results & Findings

• AI agents can be meaningfully classified as “software engineers” when they take on responsibilities such as requirement interpretation, design suggestion, code generation, and maintenance.
• Trustworthiness emerges as multi‑faceted; focusing on a single metric (e.g., test pass rate) is insufficient.
• Current evaluation ecosystems fall short: while technical quality can be measured with existing CI pipelines, dimensions like epistemic humility and societal alignment lack robust, standardized metrics.
• Ethics‑by‑design is actionable: embedding provenance logs, uncertainty quantification, and policy‑driven constraints into AI tools can bridge part of the trust gap.

Practical Implications

By adopting these practices, organizations can start to trust AI assistants where they excel (speed, pattern recognition) while keeping human oversight where nuance, ethics, or uncertainty dominate.

Limitations & Future Work

• Vision‑only: The paper does not present empirical studies or user experiments; its claims are based on conceptual analysis.
• Measurement challenges: While the trust dimensions are well‑argued, concrete, validated metrics for epistemic humility and societal alignment remain undeveloped.
• Scope of AI agents: The framework assumes relatively capable, language‑model‑based agents; applicability to narrower tools (e.g., linters) is not explored.

Future research directions suggested by the authors include: building benchmark suites that capture the full trust spectrum, conducting longitudinal studies of human‑AI SE teams, and developing governance frameworks that operationalize the ethics‑by‑design principles.

Authors

• Aldeida Aleti
• Baishakhi Ray
• Rashina Hoda
• Simin Chen

Paper Information

• arXiv ID: 2602.06310v1
• Categories: cs.SE
• Published: February 6, 2026
• PDF: Download PDF