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QRS 2020 : The 20th IEEE International Conference on Software Quality, Reliability, and Security

Vilnius, Lithuania
Event Date: Jul 27, 2020 - Jul 31, 2020
Abstract Submission Deadline: Mar 08, 2020
Submission Deadline: Mar 15, 2020
Notification of Acceptance: May 31, 2020
Camera Ready Version Due : Jun 19, 2020

About

In 2015, the SERE conference (IEEE International Conference on Software Security and Reliability) and the QSIC conference (IEEE International Conference on Quality Software) were combined into a single conference, QRS, with Q representing Quality, R for Reliability, and S for Security, sponsored by the IEEE Reliability Society. This conference provides engineers and scientists from both industry and academia a platform to present their ongoing work, relate their research outcomes and experiences, and discuss the best and most efficient techniques for the development of reliable, secure, and trustworthy systems. It also represents an excellent opportunity for the academic community to become more aware of subject areas critical to the software industry as practitioners bring their needs to the table. The 20th QRS conference will be held from July 27-31, 2020 in Vilnius, Lithuania.

Call For Paper

Topics of Interest

  • Reliability, Security, Availability, and Safety of Software Systems
  • Software Testing, Verification, and Validation
  • Program Debugging and Comprehension
  • Information and Software Quality Assurance
  • Fault Tolerance for Software Reliability Improvement
  • Modeling, Prediction, Simulation, and Evaluation
  • Metrics, Measurements, and Analysis
  • Secure and Reliable Storage
  • Software Penetration and Protection
  • Software Vulnerabilities
  • Formal Methods
  • Malware Detection and Analysis
  • Intrusion Detection and Prevention
  • Operating System Security and Reliability
  • Mobile and Smartphone Applications
  • Internet of Things and Cloud Computing
  • Information and Knowledge Management
  • Benchmark, Tools, Industrial Applications, and Empirical Studies
  • Machine Learning and Deep Learning Models and Systems

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