Trends in Computer-Aided Verification (Seminar)

Seminar in Theoretical CS, Summer 2021


  • 07.12.2020: we are online

Dates & Deadlines

14.04.2021 15:00Introduction (Zoom meeting)
18.04.2021Topic preferences due
10.05.2021Detailed outline due
07.06.2021Seminar report due
28.06.2021Presentation slides due
13.07.2021 (?)Seminar

Introduction and Assignment of Topics


The term Computer-Aided Verification refers to theory and practice of computer-supported formal analysis methods for both hardware and software systems. Likewise, it is the name of an annual academic conference on that topic. The modeling and verification of such systems is an important issue. In particular, safety-critical systems are ones in which errors can be disastrous: loss of life, major financial losses, etc. Techniques to safeguard against such scenarios are essential for such systems. Testing can identify problems, especially if done in a rigorous fashion, but is generally not sufficient to guarantee a satisfactory level of quality.   Formal methods, on the other hand, offer techniques ranging from the description of requirements in a formal notation to allow for rigorous reasoning about them, to techniques for automatic verification of software. Due to the complexity of these approaches and the systems they are applied to, automated computer support is indispensable.  

The goal of this seminar is to give an overview of the related research activities of the MOVES group. It covers several areas of computer science to which computed-aided verification techniques are central, and which represent the research fields of the respective supervisors. Each area features a number of topics which are described in a scientific journal or conference article. These research articles are the basis on which students have to prepare their report and presentation. The following list gives an overview of the areas that will be covered.


(The annotations “B” and “M” respectively refer to topics on Bachelor and Master level.)

Robustness Analysis of Neural Networks

  1. Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana: Efficient Formal Safety Analysis of Neural Networks, NeurIPS 2018 (B/M)
  2. Patrick Henriksen, Alessio Lomuscio: Efficient Neural Network Verification via Adaptive Refinement and Adversarial Search, ECAI 2020 (M)
  3. Hoang-Dung Tran, Diago Manzanas Lopez, Patrick Musau, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang: Star-Based Reachability Analysis of Deep Neural Networks, FM 2019 (M)
  4. Jianlin Li, Jiangchao Liu, Pengfei Yang, Liqian Chen, Xiaowei Huang, Lijun Zhang: Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification, SAS 2019 (B/M)
  5. Pengfei Yang, Renjue Li, Jianlin Li, Cheng-Chao Huang, Jingyi Wang, Jun Sun, Bai Xue, Lijun Zhang: Improving Neural Network Verification through Spurious Region Guided Refinement, TACAS 2021 (B/M)
  6. Michael Everett, Golnaz Habibi, Jonathan P. How: Robustness Analysis of Neural Networks via Efficient Partitioning with Applications in Control Systems, IEEE CSL 2020 (M)

Analysis of Bayesian Networks

  1. Ezio Bartocci, Laura Kovács, Miroslav Stankovic: Analysis of Bayesian Networks via Prob-Solvable Loops. ICTAC 2020: 221-241 (B/M)
  2. Andy Shih, Arthur Choi, Adnan Darwiche: Formal Verification of Bayesian Network Classifiers. PGM 2018: 427-438 (B/M)
  3. Arthur Choi, Ruocheng Wang, Adnan Darwiche: On the relative expressiveness of Bayesian and neural networks. Int. J. Approx. Reason. 113: 303-323 (2019) (M)

Synthesizing Quantitative Loop Invariants for Probabilistic Programs

  1. Yijun Feng, Lijun Zhang, David N. Jansen, Naijun Zhan, Bican Xia: Finding Polynomial Loop Invariants for Probabilistic Programs. ATVA 2017 (M)
  2. Gilles Barthe, Thomas Espitau, Luis María Ferrer Fioriti, Justin Hsu: Synthesizing Probabilistic Invariants via Doob’s Decomposition. CAV 2016 (M)
  3. Ezio Bartocci, Laura Kovács, Miroslav Stankovič: Automatic Generation of Moment-Based Invariants for Prob-Solvable Loops. ATVA 2019 (M)

Formal Approaches to Systems Engineering

  1. Ola Bäckström, Yuliya Butkova, Holger Hermanns, Jan Krčál, and Pavel Krčál: Effective Static and Dynamic Fault Tree Analysis. SAFECOMP 2016 (B)
  2. Barbara Kordy, Sjouke Mauw, Sasa Radomirovic, and Patrick Schweitzer: Attack-Defense Trees, J. Log. Comput. 24, 2014 (B)
  3. Z. Aslanyan, F. Nielson, and D. Parker: Quantitative Verification and Synthesis of Attack-Defence Scenarios. CSF 2016 (B/M)


Basic knowledge in the following areas is expected:

  • Formal languages and automata theory
  • Mathematical logic
  • Probability Theory

Previous knowledge in semantics of programming languages and concurrency theory is helpful but not mandatory


Registration to the seminar is handled via the SuPra.

Additional Material


Thomas Noll <noll at>