The 2027 International Workshop on Building Trusted Software with AI (TrustedSoftwAIr’27) seeks to bring together researchers and practitioners working on using AI to simplify building provably correct software systems across the Programming Languages, Artificial Intelligence, Human-Computer Interaction, Verification, and Software Engineering domains. TrustedSoftwAIr’27 seeks to provide a mixing pot for ideas from these diverse disciplines, and to enable cross-pollination and collaboration. The tentative date for the workshop is Monday, April 26, 2027. More information can be found at https://trustedsoftwair.github.io.
Program Committee
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Emily First (co-chair), Rutgers University
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Yuriy Brun (co-chair), University of Massachusetts
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Yousef Alhessi, University of Endinburgh
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Saikat Chakraborty, Microsoft
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Isil Dillig, University of Texas Austin
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Matthew Dwyer, Amazon & University of Virginia
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Sarah Fakhoury, Microsoft
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Harrison Goldstein, University at Buffalo
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Andrew Head, University of Pennsylvania
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Claire Le Goues, Carnegie Mellon University
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Nenad Medvidovic, University of Southern California
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Alexandra Mendes, University of Porto
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Bryan Parno, Amazon & Carnegie Mellon University
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Gabriel Poesia, University of Michigan
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Neha Rungta, Amazon
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Alex Sanchez-Stern, d model
Call for Papers
Submission Types
We invite two types of submissions: (1) novel early-stage research ideas (8 page limit) and (2) abstracts of papers published in other venues to be presented (without archival publication) at TrustedSoftwAIr’27.
Topics of Relevance
We invite submissions that discuss recent advances in the development, application, and evaluation of AI techniques for formally verifying software and building trusted software. Topics of interest include, but are not limited to:
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AI-driven proof engineering
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Specification formalization
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Neuro-symbolic and agentic approaches
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Language and theorem prover design
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AI-augmented software analysis and testing
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Trustworthy and secure code synthesis
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Explainability and correctness of AI tools
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Human-AI collaboration
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Human trust and perception of AI-written code
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Cross-pollination with AI for Mathematics
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Data and benchmark curation
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Evaluation metrics
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Model architectures and scale