Evaluating RE Practices for Explainability: Synthesizing Insights from Daimler Truck into an Explainable RE Framework Proposal

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Umm-e- Habiba, Lucas Mauser, Jonas Fritzsch, Justus Bogner, Stefan Wagner
• Venue: arXiv: cs.SE
• Published: 2026-07-13
• Collected: 2026-07-14
• URL: http://arxiv.org/abs/2607.11771v1
• PDF: https://arxiv.org/pdf/2607.11771v1
• DOI: UNKNOWN

Plain-Language Brief

A study investigates the limitations of current Requirements Engineering (RE) practices in supporting explainability requirements, particularly within industrial contexts. The research focuses on Daimler Truck and reveals recurring challenges such as ambiguity during elicitation, limited testability during specification, and fragmented validation due to vague criteria and regulatory uncertainty.

What the Document Says

• Explainability is important for AI-based systems, especially in safety-critical and regulated domains.
• Prior research has proposed frameworks, patterns, and user-centered approaches for explainability but lacks empirical understanding of RE practices supporting explainability requirements across the RE lifecycle.
• The authors conducted a study with Daimler Truck practitioners employing think-aloud protocols and moderated group discussions to investigate how explainability requirements are elicited, specified, and validated using established RE techniques.
• Preliminary analysis reveals recurring challenges across all steps: conceptual ambiguity during elicitation, limited testability and expressiveness during specification, and fragmented validation due to vague criteria and regulatory uncertainty.
• The findings indicate that current RE practices provide limited support for systematically addressing explainability requirements.

Key Concepts Mentioned

• Explainability
• Requirements Engineering (RE)
• AI-based systems
• Elicitation
• Specification
• Validation

Claims Made by the Document

• Existing RE practices provide limited support for systematically addressing explainability requirements.

Evidence Used

• Preliminary analysis of the study's findings from Daimler Truck practitioners

Methods / Approach

• Qualitative study
• Think-aloud protocols
• Moderated group discussions

Limitations Stated by the Authors

The paper reports early findings from an ongoing industry-based study.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The study was conducted with practitioners at Daimler Truck only, limiting the generalizability of the findings.

Open Questions

• How can existing RE practices be improved to better support explainability requirements across different industries?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper does not provide specific suggestions for improving current RE practices. The limitations section mentions that this is an ongoing study.

Raw Abstract / Excerpt

Explainability has emerged as a critical requirement for AI-based systems, particularly in safety-critical and regulated domains. Although prior research has proposed frameworks, patterns, and user-centered approaches to support explainability, there is limited empirical understanding of how existing Requirements Engineering (RE) practices support explainability requirements across the RE lifecycle, especially in an industrial context. This paper reports early findings from an ongoing industry-based study investigating how explainability requirements are elicited, specified, and validated using established RE techniques. We conducted a multi-phase qualitative study with eight practitioners at Daimler Truck, employing think-aloud protocols and moderated group discussions across requirements elicitation, specification, and validation steps. Our preliminary analysis reveals recurring challenges across all steps, including conceptual ambiguity during elicitation, limited testability and expressiveness during specification, and fragmented validation due to vague criteria and regulatory uncertainty. These findings indicate that current RE practices provide limited support to systematically address explainability requirements. The paper contributes empirical insights into step-specific and cross-cutting challenges and outlines a research vision toward developing an empirically grounded RE framework for explainable AI-based systems.