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Cited by 5Software Engineering Techniques and PracticesHuman-Automation Interaction and SafetySoftware Engineering Research

On the Need to Rethink Trust in AI Assistants for Software Development: A Critical Review

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Baltes, Sebastian, Speith, Timo, Chiteri, Brenda, Mohsenimofidi, Seyedmoein, Chakraborty, Shalini, Buschek, Daniel·2026·IEEE Transactions on Software Engineering
DOI 10.1109/tse.2026.3659804
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Abstract

Trust is a fundamental concept in human decision-making and collaboration that has long been studied in philosophy and psychology. However, software engineering (SE) articles often use the termtrustinformally; providing an explicit definition or embedding results in established trust models is rare. In SE research on AI assistants, this practice culminates in equating trust with the likelihood of accepting generated content, which, in isolation, does not capture the full conceptual complexity of trust. Without a common definition, true secondary research on trust is impossible. The objectives of our research were: (1) to present the psychological and philosophical foundations of human trust, (2) to systematically study how trust is conceptualized in SE and the related disciplines human-computer interaction and information systems, and (3) to discuss limitations of equating trust with content acceptance, outlining how SE research can adopt existing trust models to overcome the widespread informal use of the term trust. We conducted a literature review across disciplines and a critical review of recent SE articles with a focus on trust conceptualizations. We found that trust is rarely defined or conceptualized in SE articles. Related disciplines commonly embed their methodology and results in established trust models, clearly distinguishing, for example, betweeninitial trustandtrust formationand betweenappropriateandinappropriate trust. On a meta-scientific level, other disciplines even discuss whether and when trust can be applied to AI assistants at all. Our study reveals a significant maturity gap of trust research in SE compared to other disciplines. We provide concrete recommendations on how SE researchers can adopt established trust models and instruments to study trust in AI assistants beyond the acceptance of generated software artifacts.

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  • These differences in behaviour, despite mistrust, support the claims that acceptance of generated software artifacts does not reflect trust [24].

    “These differences in behaviour, despite mistrust, support the claims that acceptance of generated software artifacts does not reflect trust [24].”

    From Same Scrutiny, More Time: Eye Tracking Insights into Reviewing LLM-Labelled Code (2026) · cites this paper as [24]

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Cited by

Sorted by citations

Authors
Baltes, S., Speith, T., Chiteri, B., Mohsenimofidi, S., Chakraborty, S., & Buschek, D.
Affiliations
  • Heidelberg University (DE)
  • University of Bayreuth (DE)
ORCID
Venue
IEEE Transactions on Software Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2026-04
Volume
52
Issue
4
Pages
1265-1281
ISSN
  • 0098-5589 (ISSN-L)
  • 1939-3520
  • 2326-3881
DOI
10.1109/tse.2026.3659804
OpenAlex
W7126171853
Cited by
5
References
27
Citations by year
2026: 3 · 2025: 2
FWCI
13.46
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Software Engineering (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
Keywords
trust model, trust formation, initial trust

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