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What Does It Take to Get Guarantees? Systematizing Assumptions in Cyber-Physical Systems

Chengyu Li, Saleh Faghfoorian, Ivan Ruchkin

TL;DR

This paper tackles the fragmentation in how CPS guarantees are derived from underlying assumptions by conducting a grounded-theory survey of 104 CPS papers (2014–2024). It builds a taxonomy and codebooks for assumptions and guarantees, labeling 423 assumptions, 321 guarantees, and 2,299 language-feature instances, to reveal how guarantees are anchored to abstract models and how often essential aspects like sensing, perception, and neural components are underspecified. Key findings show that modeling/abstraction assumptions dominate, safety is the most common guarantee but often limited by initialization, and information-theoretic or probabilistic uncertainty is rarely expressed explicitly. The study provides a public dataset and practical calls to action to improve initialization reporting, sensing/perception constraints, neural-assumption specificity, and systematic uncertainty reporting, aiming to enhance reproducibility and real-world transfer of CPS guarantees.

Abstract

Formal guarantees for cyber-physical systems (CPS) rely on diverse assumptions. If satisfied, these assumptions enable the transfer of abstract guarantees into real-world assurances about the deployed CPS. Although assumptions are central to assured CPS, there is little systematic knowledge about what assumptions are made, what guarantees they support, and what it would take to specify them precisely. To fill this gap, we present a survey of assumptions and guarantees in the control, verification, and runtime assurance areas of CPS literature. From 104 papers over a 10-year span (2014-2024), we extracted 423 assumptions and 321 guarantees using grounded-theory coding. We also annotated the assumptions with 21 tags indicating elementary language features needed for specifications. Our analysis highlighted prevalent trends and gaps in CPS assumptions, particularly related to initialization, sensing, perception, neural components, and uncertainty. Our observations culminated in a call to action on reporting and testing CPS assumptions.

What Does It Take to Get Guarantees? Systematizing Assumptions in Cyber-Physical Systems

TL;DR

This paper tackles the fragmentation in how CPS guarantees are derived from underlying assumptions by conducting a grounded-theory survey of 104 CPS papers (2014–2024). It builds a taxonomy and codebooks for assumptions and guarantees, labeling 423 assumptions, 321 guarantees, and 2,299 language-feature instances, to reveal how guarantees are anchored to abstract models and how often essential aspects like sensing, perception, and neural components are underspecified. Key findings show that modeling/abstraction assumptions dominate, safety is the most common guarantee but often limited by initialization, and information-theoretic or probabilistic uncertainty is rarely expressed explicitly. The study provides a public dataset and practical calls to action to improve initialization reporting, sensing/perception constraints, neural-assumption specificity, and systematic uncertainty reporting, aiming to enhance reproducibility and real-world transfer of CPS guarantees.

Abstract

Formal guarantees for cyber-physical systems (CPS) rely on diverse assumptions. If satisfied, these assumptions enable the transfer of abstract guarantees into real-world assurances about the deployed CPS. Although assumptions are central to assured CPS, there is little systematic knowledge about what assumptions are made, what guarantees they support, and what it would take to specify them precisely. To fill this gap, we present a survey of assumptions and guarantees in the control, verification, and runtime assurance areas of CPS literature. From 104 papers over a 10-year span (2014-2024), we extracted 423 assumptions and 321 guarantees using grounded-theory coding. We also annotated the assumptions with 21 tags indicating elementary language features needed for specifications. Our analysis highlighted prevalent trends and gaps in CPS assumptions, particularly related to initialization, sensing, perception, neural components, and uncertainty. Our observations culminated in a call to action on reporting and testing CPS assumptions.

Paper Structure

This paper contains 34 sections, 6 equations, 8 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Survey Methodology
  3. Survey Scope
  4. CPS Components in Scope
  5. Definitions of Assumptions and Guarantees
  6. Survey Structure and Coding Methodology
  7. Corpus
  8. Coding Process
  9. Open Coding: Identifying Assumptions and Guarantees
  10. Axial Coding: Assigning Tags and Language Features
  11. Selective Coding: Categorizing Assumptions Tags
  12. Summary of Corpus
  13. Distributions of Assumptions
  14. Distribution of Guarantees
  15. Language Features for Assumptions
  16. ...and 19 more sections

Figures (8)

  • Figure 1: Key concepts of our survey and their relationships; 1 means a "single concept", $\ast$ means "one or more concepts".
  • Figure 2: Publication venues of the 104 surveyed papers.
  • Figure 3: Survey workflow from papers to analysis.
  • Figure 4: Distribution of assumption tags and categories across the corpus ($N{=}637$). The inner ring shows the share of each high-level category; the outer ring breaks each category into its constituent tags.
  • Figure 5: Distribution of guarantee tags across the corpus ($N{=}496$).
  • ...and 3 more figures

Theorems & Definitions (4)

  • definition 1: Universe of Discourse
  • definition 2: Abstract Guarantee
  • definition 3: Operational Guarantee
  • definition 4: Assumption