Course details

Static Analysis and Verification

SAV Acad. year 2022/2023 Winter semester 5 credits

Current academic year

Introduction of basic terms, such as analysis and verification, formal analysis and verification, soundness and completeness, logical and physical time, safety and liveness, etc. Overview of various approaches to static analysis and verification and other alternative verification approaches. Introduction to temporal logics as one of the classical means of specification of desired system properties. Model checking for the LTL logic using Büchi automata. Use of automatically refined predicate abstraction as one of the most successful approaches towards model checking of software. Abstract interpretation as one of the most successful methods of static analysis: principles, algorithms, and an overview of the most prominent abstract domains. Data flow analysis: basic terms and principles, classical analyses used in optimizing compilers, design of new analyses, pointer analyses. Solving of the SAT and SMT problems, which are used (not only) within a lot of verification approaches. Verification based on symbolic execution, bounded model checking, and k-induction. Deductive verification of annotated programs (functions' pre- and postconditions, loop invariants). Binary decision diagrams as a means of efficient storage of (not only) state spaces. Introduction to automatic verification of termination of program runs (absence of looping) and automatic analysis of complexity.


Course coordinator

Language of instruction




Time span

  • 39 hrs lectures
  • 13 hrs projects

Assessment points

  • 70 pts final exam
  • 30 pts projects



Course Web Pages

Subject specific learning outcomes and competences

Students are acquainted with principles and methods of static analysis and verification and with their application within the process of designing computer systems. Students know capabilities and the basic ways of using computer-aided tools for static analysis and verification. Acquired knowledge about the significance and possibilities of using methods and tools of static analysis and verification within the development of various kinds of systems and about their growing use in practice.

Learning objectives

The goal of the course is to get students acquainted with various methods of static analysis and verification that are commonly used in practice for finding bugs or proving correctness of systems. Students will be introduced to a variety of methods of static analysis and verification with their advantages and disadvantages. Moreover, the course will also present an overview of current tools that implement the discussed techniques and students will, through a project, obtain a first-hand practical experience with a chosen tool.

Why is the course taught

The course will introduce students to methods and tools of static analysis and verification, which are growing in use for assuring quality of computer systems. The knowledge of these methods is beneficial for designers of software, as well as hardware, and is of high importance especially for a work within the area of quality assurance. 

Prerequisite knowledge and skills

Knowledge of discrete mathematics, the theory of formal languages, and algorithmics on the bachelor's level is assumed.

Study literature

  • Aho, A.V., Lam, S., Sethi, R., Ullman, J.D.: Compilers: Principles, Techniques, and Tools. Addison Wesley, 2nd ed., 2006. The part devoted to static analysis.
  • Bradley, A.R., Manna, Z.: The Calculus of Computation: Decision Procedures with Applications to Verification, Springer, 2007.
  • Valmari, A.: The State Explosion Problem. In Reisig, W., Rozenberg, G.: Lectures on Petri Nets I: Basic Models, volume 1491 of Lecture Notes in Computer Science, pages 429-528. Springer-Verlag, 1998.
  • Chess, B., West,J.: Secure Programming with Static Analysis. Addison-Wesley Professional, 2007.
  • Kroening, D., Strichman, O.: Decision Procedures: An Algorithmic Point of View, Springer, 2008.
  • Holzmann, G.J.: The SPIN Model Checker: Primer and Reference Manual, Addison-Wesley Professional, 2003.
  • Ben-Ari, M.: Principles of the Spin Model Checker, Springer, 2008.
  • Bertot Y., Castéran, P.: Interactive Theorem Proving and Program Development: Coq'Art: The Calculus of Inductive Constructions, Springer, 2010.
  • Materiály aktuálně volně dostupné na Internetu, a to zejména články a dokumentace týkající se počítačových nástrojů pro statickou analýzu a verifikaci.
  • Rival, X., Yi, K. Introduction to Static Analysis: An Abstract Interpretation Perspective. MIT Press, 2020.
  • Clarke, E.M., Henzinger, Th.A., Veith, H., Bloem, R. (Eds.): Handbook of Model Checking, Springer International Publishing, 2018.
  • Baier, C., Katoen, J.-P.: Principles of Model Checking. MIT Press, 2008.
  • Nielson, F., Nielson, H.R., Hankin, C.: Principles of Program Analysis, Springer-Verlag, 2005.
  • Edmund, M.C., Grumberg, O., Peled, D.A.: Model Checking. MIT Press, 2000.
  • Khedker, U., Sanyal, A., Sathe, B.: Data Flow Analysis: Theory and Practice, CRC Press, 2009.

Syllabus of lectures

  1. Notion of the terms analysis and verification. Classification of verified properties and systems. Overview of approaches to formal analysis and verification.
  2. Temporal logics CTL*, CTL, and LTL.
  3. Model checking of systems with properties specified in LTL using Büchiho automata.
  4. Model checking using predicate abstraction refined by exclusion of spurious counterexamples.
  5. Abstract Interpretation I: basic notions and principles.
  6. Abstract Interpretation II: an overview of practically successful abstract domains.
  7. Basic notions and principles of data flow analysis, classical data flow analyses.
  8. Advanced data flow analyses, pointer analyses.
  9. Verification of software using symbolic execution.
  10. Deductive verification of annotated programs.
  11. Solutions of the SAT and SMT problems as the enabling technology of many approaches to analysis and verification.
  12. Binary Decision Diagrams.
  13. Verification of termination of programs, automatic analysis of computational complexity.

Syllabus - others, projects and individual work of students

First-hand practical experience with a selected tool for static analysis or verification and the principles it is based on, reproduction of case studies available for the tool, student's own experiments with the tool, and composition of a report about the tool and the performed experiments.

Progress assessment

  • An evaluated project for 30 points. 
  • A final examination for 70 points. 
  • To be allowed to sit for the written examination, a student is to earn at least 15 points during the semester.

Controlled instruction

A project focused at obtaining first-hand practical experience with a selected tool for static analysis or verification and the principles it is based on, reproduction of case studies available for the tool, student's own experiments with the tool, and composition of a report about the tool and the performed experiments.

Exam prerequisites

Having at least 50 % of the possible point evaluation of the project.

How to contact the teacher

Primary contact by email by means of which individual consultations can be arranged.

Course inclusion in study plans

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