Ranking & Metrics Conference Call for Papers Other Conferences in United States
SEFM 2021 : Software Engineering and Formal Methods

SEFM 2021 : Software Engineering and Formal Methods

Online, Online

Submission Deadline: Monday 26 Jul 2021

Conference Dates: Dec 06, 2021 - Dec 10, 2021

Impact Score 2.64


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Ranking & Metrics Impact Score is a novel metric devised to rank conferences based on the number of contributing top scientists in addition to the h-index estimated from the scientific papers published by top scientists. See more details on our methodology page.

Research Impact Score: 2.64
Contributing Top Scientist: 23
Papers published by Top Scientists 21
Research Ranking (Computer Science) 310

Conference Call for Papers

The topics of interest include, but are not limited to, the following aspects of software engineering and formal methods.

Software Development Methods
Formal modeling, specification, and design
Software evolution, maintenance, re-engineering, and reuse
Design Principles
Programming languages
Domain-specific languages
Type theory
Abstraction and refinement
Software Testing, Validation, and Verification
Model checking, theorem proving, and decision procedures
Testing and runtime verification
Statistical and probabilistic analysis
Software synthesis
Lightweight and scalable formal methods
Analysis of dependability, performance and other non-functional properties
Resilience, security, privacy, and trust
Safety-critical, fault-tolerant, and secure systems
Software assurance and certification
Applications and Technology Transfer
Self-adaptive systems
Service-oriented and cloud computing systems, Internet of Things
Component-based and multi-agent systems
Real-time, hybrid, and cyber-physical systems
Intelligent systems and machine learning
HCI, interactive systems, and human error analysis
Special topic at SEFM 2021: “Software Engineering and Formal Methods for Resilient and Trustworthy Autonomous Systems”
Resilient autonomous systems
Verification and assurance of autonomous systems
Mitigation of uncertainty and disruption in autonomous systems
Integration of conventional software and machine learning
Assurance patterns and assurance cases for autonomous systems
Case studies, best practices, and experience reports

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