About this Research Topic
Formal methods are mathematically rigorous techniques for the specification, development, and verification of hardware and software systems. They provide a robust mathematical framework for system specification and verification that contributes to the reliability and robustness of a design. Over the years, formal methods have been applied to design several hardware and software systems including the NASA NextGen Air Traffic Transportation Systems, several microprocessor designs from major hardware vendors, such as Intel, IBM and AMD, and large distributed systems and secure-software repositories of major software technology companies, such as Facebook and Amazon. However, the inherent computational complexity limits the widespread applicability of formal methods due to the “state-space explosion problem.” Several mathematical and algorithmic advances have helped scale the application of formal methods to the design of complex systems, with most research focused on logic for system specification and verification, abstraction and composition techniques, and decision procedures for different mathematical theories.
State-of-the-art advances in formal methods and widespread adoption of cloud computing has led to an increased interest in the use of parallel computing infrastructures to tackle the scalability limitations of formal methods. High-performance computing (HPC) is a key-enabler for scaling the applicability of formal methods to large and complex problems, besides fundamental algorithmic advances. HPC can enable both vertical scalability, e.g., running multiple “independent” tasks in parallel, and horizontal scalability, e.g., breaking up an individual task into smaller “co-dependent” parallel tasks. It is well-known that formal-methods algorithms are computationally-expensive, e.g., model checking, and major algorithmic advances are rare due to the inherent problem complexity. Therefore, the successful application of HPC to formal methods will take advantage of high-performance computing platforms for the verification of complex systems by utilizing parallel algorithms for verification and synthesis (e.g., parallel model checking algorithms), various divide-and-conquer decision procedures (e.g., parallel SAT solving), and parallel problem orchestration (e.g., efficient distribution of individual tasks across a compute cluster).
This Research Topic welcomes submissions of papers reporting both original research and case studies on algorithmic advances and the use of high-performance computing for scaling automated reasoning and application of formal methods in system verification and synthesis, and mathematical and logical foundations of parallel formal verification and synthesis algorithms including contributions discussing complexity and correctness guarantees of parallel algorithms. Topics of interest include (but are not limited to):
● Algorithms and tools for verifying models and implementations; including model-checking, theorem proving, runtime verification, and decision procedures
● Algorithms and tools for system synthesis; including software synthesis and reactive (discrete-time, timed, hybrid) synthesis
● Mathematical and logical foundations of scalable formal verification and synthesis algorithms; including complexity analysis, correctness guarantees, and performance improvements
● Case studies and experience reports on the use of high-performance computing in applying formal methods for system verification and synthesis.
State-of-the-art advances in formal methods and widespread adoption of cloud computing has led to an increased interest in the use of parallel computing infrastructures to tackle the scalability limitations of formal methods. High-performance computing (HPC) is a key-enabler for scaling the applicability of formal methods to large and complex problems, besides fundamental algorithmic advances. HPC can enable both vertical scalability, e.g., running multiple “independent” tasks in parallel, and horizontal scalability, e.g., breaking up an individual task into smaller “co-dependent” parallel tasks. It is well-known that formal-methods algorithms are computationally-expensive, e.g., model checking, and major algorithmic advances are rare due to the inherent problem complexity. Therefore, the successful application of HPC to formal methods will take advantage of high-performance computing platforms for the verification of complex systems by utilizing parallel algorithms for verification and synthesis (e.g., parallel model checking algorithms), various divide-and-conquer decision procedures (e.g., parallel SAT solving), and parallel problem orchestration (e.g., efficient distribution of individual tasks across a compute cluster).
This Research Topic welcomes submissions of papers reporting both original research and case studies on algorithmic advances and the use of high-performance computing for scaling automated reasoning and application of formal methods in system verification and synthesis, and mathematical and logical foundations of parallel formal verification and synthesis algorithms including contributions discussing complexity and correctness guarantees of parallel algorithms. Topics of interest include (but are not limited to):
● Algorithms and tools for verifying models and implementations; including model-checking, theorem proving, runtime verification, and decision procedures
● Algorithms and tools for system synthesis; including software synthesis and reactive (discrete-time, timed, hybrid) synthesis
● Mathematical and logical foundations of scalable formal verification and synthesis algorithms; including complexity analysis, correctness guarantees, and performance improvements
● Case studies and experience reports on the use of high-performance computing in applying formal methods for system verification and synthesis.
Keywords: formal verification, formal synthesis, automated reasoning, decision procedures, parallel algorithms, theoretical foundations
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