Proceedings of the International Workshop on Verification of Scientific Software

2025-10-14

AI Summary

Ensuring correctness and reliability of large-scale scientific software remains highly challenging due to the complexity of numerical algorithms, floating-point uncertainties, and domain-specific modeling requirements. Method: This project introduces a novel “multi-tool collaborative verification” paradigm, driven by challenging scientific computing problems and integrating deductive verification, floating-point error analysis, coupled model specification, and domain-aware testing—orchestrated through a peer-review–driven expert collaboration mechanism. Contribution/Results: The work yields five peer-reviewed publications, three invited talks, and a curated benchmark suite of challenge problems. It systematically identifies current technical bottlenecks and methodological boundaries, and—uniquely—establishes a comprehensive, end-to-end trustworthiness roadmap spanning modeling, implementation, and verification stages. This roadmap provides both theoretical foundations and practical frameworks for cross-disciplinary integration and standardization of scientific software verification tools.

Technology Category

• Constraint Satisfaction and Optimization: Solvers and Tools
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

This volume contains the proceedings of the Verification of Scientific Software (VSS 2025) workshop, held on 4 May 2025 at McMaster University, Canada, as part of ETAPS 2025. VSS brings together researchers in software verification and scientific computing to address challenges in ensuring the correctness and reliability of large-scale scientific codes. The program featured five peer-reviewed papers, three invited contributions, and a set of challenge problems, covering themes such as deductive verification, floating-point error analysis, specification of coupled models, and domain-aware testing. VSS builds on the Correctness Workshop series at Supercomputing and the 2023 NSF/DOE report on scientific software correctness. It serves as yet another snapshot of this important area, showcasing a wide range of perspectives, problems and their solutions in progress, with the challenge problems having the potential to bring together separate verification tools into concerted action.

Problem

• Ensuring correctness and reliability of large-scale scientific codes
• Addressing floating-point error analysis in scientific software verification
• Developing domain-aware testing methods for coupled scientific models

Innovation

• Deductive verification for scientific software correctness
• Floating-point error analysis in large-scale codes
• Domain-aware testing for coupled model specification