Department of Distributed and Dependable Systems

School of Computer Science, Faculty of Mathematics and Physics, Charles University

Our department is responsible for research and education in the advanced techniques for building reliable component-based software especially in the field of distributed and dependable systems, spanning from smart cyber-physical systems and IoT to cloud-based backends. Our research comprises advanced software architectures, adaptation, model-driven development, model-based testing, formal verification of software, performance measurement and modeling.

The vision pursued by our department is that of high-quality scientific research with strong industrial relevance, with the aim to enable rapid and cost-efficient production of complex and reliable software systems. To achieve this vision, our department actively collaborates on the national and international level with both academic and industrial partners.

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News From the Department

A Combined Approach to Performance Regression Testing Resource Usage Reduction

26 June 2025

Our paper A Combined Approach to Performance Regression Testing Resource Usage Reduction by M. Abdullah, D. Reichelt, V. Horký, L. Bulej, T. Bureš, P. Tůma has been accepted to Proceedings of the 21st International Conference on Predictive Models and Data Analytics in Software Engineering.

Reusing Highly Optimized IR in Dynamic Compilation

1 June 2025

Our paper Reusing Highly Optimized IR in Dynamic Compilation by A. Pečimúth, D. Leopoldseder, P. Tůma has been accepted to 39th European Conference on Object-Oriented Programming (ECOOP 2025).

Best paper award

25 May 2025

EASST Best Paper award at the TACAS 2026 conference was granted to R. Otoni, et al. for their paper Unsatisfiability Proofs for Horn Solving.

Unsatisfiability Proofs for Horn Solving

1 May 2025

Our paper Unsatisfiability Proofs for Horn Solving by R. Otoni, M. Blicha, M. Rivera, P. Eugster, J. Kofroň, N. Sharygina has been accepted to Tools and Algorithms for the Construction and Analysis of Systems.

A Model-Based Approach to Experiment-Driven Evolution of ML Workflows:

1 January 2025

Our paper A Model-Based Approach to Experiment-Driven Evolution of ML Workflows: by P. Hnětynka, T. Bureš, I. Gerostathopoulos, M. Abdullah, K. Rajenthiram has been accepted to Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering.