Toward Practical Deductive Verification: Insights from a Qualitative Survey in Industry and Academia

2025-10-23

AI Summary

Despite its efficacy in isolated projects, deductive verification has yet to achieve broad industrial adoption. To identify root barriers and key enablers, this paper conducts semi-structured interviews with 30 practitioners, followed by thematic analysis. We systematically uncover fundamental obstacles—including high proof maintenance overhead, limited automation, poor tool usability, and lack of workflow integration—as well as critical enabling factors. Diverging from prior work, we empirically establish *usability* and *workflow adaptability* as core dimensions governing adoption. Based on these findings, we propose three actionable improvement principles: (1) enhancing automation support for proof construction and evolution; (2) reducing proof maintenance burden through modularization and abstraction; and (3) deepening integration with IDEs and CI/CD pipelines. Our empirically grounded insights provide concrete, evidence-based guidance for tool developers, practitioners, and researchers—bridging the gap between academic verification techniques and engineering practice.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Deductive verification is an effective method to ensure that a given system exposes the intended behavior. In spite of its proven usefulness and feasibility in selected projects, deductive verification is still not a mainstream technique. To pave the way to widespread use, we present a study investigating the factors enabling successful applications of deductive verification and the underlying issues preventing broader adoption. We conducted semi-structured interviews with 30 practitioners of verification from both industry and academia and systematically analyzed the collected data employing a thematic analysis approach. Beside empirically confirming familiar challenges, e.g., the high level of expertise needed for conducting formal proofs, our data reveal several underexplored obstacles, such as proof maintenance, insufficient control over automation, and usability concerns. We further use the results from our data analysis to extract enablers and barriers for deductive verification and formulate concrete recommendations for practitioners, tool builders, and researchers, including principles for usability, automation, and integration with existing workflows.

Problem

• Investigating barriers to mainstream adoption of deductive verification methods
• Identifying underexplored obstacles like proof maintenance and usability issues
• Developing recommendations for practitioners and tool builders to improve adoption

Innovation

• Conducted semi-structured interviews with verification practitioners
• Employed thematic analysis to identify verification challenges
• Formulated recommendations for usability and integration workflows