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QRS 2022 : 22nd IEEE International Conference on Software Quality, Reliability, and Security


When Dec 5, 2022 - Dec 9, 2022
Where Guangzhou, China
Abstract Registration Due Aug 24, 2022
Submission Deadline Aug 31, 2022
Notification Due Oct 20, 2022
Final Version Due Nov 10, 2022
Categories    software quality   software reliability   software security   software safety

Call For Papers

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.

QRS 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 22nd QRS conference will be held from December 5-9, 2022 in Guangzhou, China.

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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