Module English

1

Module Handbook for Master of Science Software Systems Engineering

2 Revision: 13.06.2013 02:26:16

(Index of Contents)

3

Degree Course Information: Master of Science Software Systems Engineering [MSSSE/11]

Title Master of Science Software Systems Engineering

Short Title SSE (M.Sc.) Link to Further Information

http://dbis.rwth-aachen.de/SSE/

4 Revision: 13.06.2013 02:26:16

Module: Network Algorithms [MSSSE-1101101/11] Module Title Network Algorithms

Short Title Network Algorithms

Semester of Study 1

Duration (semesters) 1 Course Cycle (every n semesters)

0

Content

Routing algorithms for interconnected parallel computers Sorting networks Randomized methods for contention resolution and congestion avoidance

Algorithms for wireless networks Data management in networks Theory of peer-to-peer networks

Aims and Learning Outcomes

Knowledge about the theory of algorithms for computer networks Ability to model and analyze algorithmic problems arising in computer networks Knowledge about fundamental algorithmic design principles like randomized contention

resolution and congestion avoidance

Prerequisites Basic knowledge about algorithms, discrete structures and probability theory

Course Texts

Zur Vorlesung gibt es ein Skript. Empfohlene Bcher

F.T. Leighton. Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes Computer Networking: A Top-Down Approach Featuring the Internet. Addison Wesley

Longman, 1999.

Language of Instruction Englisch

Module Coordinator Berthold Vcking

Credits 6

Contact Hours per week 4

Self-Study Time (h) 120 Relevance to Degree Programme

Degree elective

Assessment and Qualifications Title Ref. Code Credits Credits

Workload Contact hours (h)

Self-Study Time (h)

Lecture Network Algorithms MSSSE-1101101.a/11

0 4 3 75

Exercise Network Algorithms

MSSSE-1101101.b/11

0 2 1 45

5

Masterexam Network Algorithms

MSSSE-1101101.c/11

6 0 0 0

Assessment: Lecture Network Algorithms [MSSSE-1101101.a/11] Title Lecture Network Algorithms

Short Title Lecture Network Algorithms

Semester of Study 1

Content see module description

Relevance to Degree Programme

Degree elective

Assessment: Exercise Network Algorithms [MSSSE-1101101.b/11] Title Exercise Network Algorithms

Short Title Exercise Network Algorithms

Semester of Study 1



Degree elective

Assessment: Masterexam Network Algorithms [MSSSE-1101101.c/11] Title Masterexam Network Algorithms

Short Title Masterexam Network Algorithms

Semester of Study 1



Degree elective

6 Revision: 13.06.2013 02:26:16

Module: Algorithmic Game Theory [MSSSE-1101102/11] Module Title Algorithmic Game Theory

Short Title Algorithmic Game Theory

Semester of Study 1


0

Start of Cycle Variable

Content

Introduction to game theory Complexity of game theoretic solution concepts Congestion and potential games Price of anarchy Algorithmic aspects of mechanism design


Knowledge of basic game theoretic solution concepts and their complexity Critical understanding of the basic game theoretical assumptions Ability to model problems using game theoretic approaches for the design and analysis of

algorithms and networks

Prerequisites Basic knowledge about algorithms, discrete structures, probability theory (stochastic) Course Texts

N. Nisan, T. Roughgarden, E. Tardos, V. Vazirani. Algorithmic Game Theory, Cambridge University Press, 2007. T. Roughgarden. Selfish Routing and the Price of Anarchy. MIT Press, 2005. A. Mas-Colell, M.D. Whinston, and J.R. Green. Microeconomic Theory. Oxford University

Press, 1995. M.J. Osborne. An Introduction to Game Theory. Oxford University Press. 2004.



Credits 6



Degree elective



Self-Study Time (h)

Lecture Algorithmic Game Theory

MSSSE-1101102.a/11

0 4 3 75

Exercise Algorithmic Game Theory

MSSSE-1101102.b/11

0 2 1 45

7

Masterexam Algorithmic Game Theory

MSSSE-1101102.c/11

6 0 0 0

Assessment: Lecture Algorithmic Game Theory [MSSSE-1101102.a/11] Title Lecture Algorithmic Game Theory

Short Title Lecture Algorithmic Game Theory

Semester of Study 1



Degree elective

Assessment: Exercise Algorithmic Game Theory [MSSSE-1101102.b/11] Title Exercise Algorithmic Game Theory

Short Title Exercise Algorithmic Game Theory

Semester of Study 1



Degree elective

Assessment: Masterexam Algorithmic Game Theory [MSSSE-1101102.c/11] Title Masterexam Algorithmic Game Theory

Short Title Masterexam Algorithmic Game Theory

Semester of Study 1



Degree elective

8 Revision: 13.06.2013 02:26:16

Module: Algorithmic Cryptography [MSSSE-1101103/11] Module Title Algorithmic Cryptography

Short Title Algorithmic Cryptography

Semester of Study 1


0

Course Texts Zur Vorlesung wird ein Skript erstellt und folgende Literatur empfohlen:

H. Delfs, H. Knebl: Introduction to Cryptography. Springer 2002 A. Salomaa: Public-Key Cryptography. Springer 1996. F.L. Bauer: Entzifferte Geheimnisse. Springer 2000.


Module Coordinator Walter Unger

Credits 6



Degree elective



Self-Study Time (h)

Lecure Algorithmic Cryptography

MSSSE-1101103.a/11

0 4 3 75

Exercise Algorithmic Cryptography

MSSSE-1101103.b/11

0 2 2 30

Masterexam Algorithmic Cryptography

MSSSE-1101103.c/11

6 0 0 0

Assessment: Lecure Algorithmic Cryptography [MSSSE-1101103.a/11] Title Lecure Algorithmic Cryptography

Short Title Lecture Algorithmic Cryptography

Semester of Study 1



Degree elective

Assessment: Exercise Algorithmic Cryptography [MSSSE-1101103.b/11] Title Exercise Algorithmic Cryptography

Short Title Exercise Algorithmic Cryptography

Semester of Study 1


9


Degree elective

Assessment: Masterexam Algorithmic Cryptography [MSSSE-1101103.c/11] Title Masterexam Algorithmic Cryptography

Short Title Masterexam Algorithmic Cryptography

Semester of Study 1



Degree elective

10 Revision: 13.06.2013 02:26:16

Module: Graph Algorithms [MSSSE-1101104/11] Module Title Graph Algorithms

Short Title Graph Algorithms

Semester of Study 1


0

Course Texts Zur Vorlesung wird ein Skript erstellt und folgende Literatur empfohlen:

Golumbic M.C. Algorithmic Graph Theory and Perfect Graphs Harary F.: Graphentheorie, 1974. Wilson R.J.: Einfhrung in die Graphentheorie, 1972


Module Coordinator Walter Unger

Credits 6



Degree elective



Self-Study Time (h)

Lecture Graph Algorithms MSSSE-1101104.a/11

0 4 3 75

Exercise Graph Algorithms MSSSE-1101104.b/11

0 2 2 30

Masterexam Graph Algorithms

MSSSE-1101104.c/11

6 0 0 0

Assessment: Lecture Graph Algorithms [MSSSE-1101104.a/11] Title Lecture Graph Algorithms

Short Title Lecture Graph Algorithms

Semester of Study 1

Content see module decsription


Degree elective

Assessment: Exercise Graph Algorithms [MSSSE-1101104.b/11] Title Exercise Graph Algorithms

Short Title Exercise Graph Algorithms

Semester of Study 1


11


Degree elective

Assessment: Masterexam Graph Algorithms [MSSSE-1101104.c/11] Title Masterexam Graph Algorithms

Short Title Masterexam Graph Algorithms

Semester of Study 1



Degree elective

12 Revision: 13.06.2013 02:26:16

Module: Theoretical Foundations of Distributed Systems [MSSSE-1101106/11] Module Title Theoretical Foundations of Distributed Systems

Short Title Theoretical Foundations of Distributed Systems

Semester of Study 1


2

Content

Routing in networks: centralized and distributed approaches Randomized methods for contention resolution and congestion avoidance

Queueing Theory (stochastic and adversarial) Game theoretic models (esp. congestion games) Distributed Hash Tables Peer-2-Peer Networks (Chord) Wireless networks (Yao graph, broadcasting, SINR model)


Knowledge about the theoretical foundations of distributed systems with a focus on algorithmic problems and solutions Ability to model algorithmic problems arising in distributed systems Knowledge about fundamental algorithmic design principles like randomized contention

resolution and congestion avoidance

Prerequisites Basic knowledge about algorithms, discrete structures, and probability theory

Course Texts

Folien und Skripte Empfohlene Bcher Leighton. Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes Kurose, Ross: Computer Networking: A Top-Down Approach Featuring the Internet.

Addison Wesley Longman, 1999. Kleinberg, Tardos: Algorithm Design, Addison Wesley Pearson, 2005

Language of Instruction English


Credits 6



Degree elective



Self-Study Time (h)

Lecture Theory of Distributed Systems

MSSSE-1101106.a/11

0 4 3 75

13

Exercise Theory of Distributed Systems

MSSSE-1101106.b/11

0 2 2 30

Masterexam Theory of Distributed Systems

MSSSE-1101106.c/11

6 0 0 0

Assessment: Lecture Theory of Distributed Systems [MSSSE-1101106.a/11] Title Lecture Theory of Distributed Systems

Short Title Lecture Theory of Distributed Systems

Semester of Study 1


Degree elective

Assessment: Exercise Theory of Distributed Systems [MSSSE-1101106.b/11] Title Exercise Theory of Distributed Systems

Short Title Exercise Theory of Distributed Systems

Semester of Study 1


Degree elective

Assessment: Masterexam Theory of Distributed Systems [MSSSE-1101106.c/11] Title Masterexam Theory of Distributed Systems

Short Title Masterexam Theory of Distributed Systems

Semester of Study 1


Degree elective

14 Revision: 13.06.2013 02:26:16

Module: Analysis of Algorithms [MSSSE-1101201/11] Module Title Analysis of Algorithms

Short Title Analysis of Algorithms

Semester of Study 1


0

Content In this lecture, you learn about basic techniques for the analysis of algorithms and apply them on numerous examples. On one hand, some algorithms will be analyzed in great detail -- culminating in the exact number of machine instructions executed on average --, on the other hand you learn how to get asymptotic estimates of the running time with very little effort.


Decomposing algorithms into their basic blocks and findingvrecurrence relations for the number of times they are executed Elementary methods for the solution of these recurrence relations Mathematical techniques for the analysis of algorithms, in particular generating functions,

singularity analysis, and saddle point method Gaining experience in the analysis of algorithms by applying all these methods on

numerous practical examples

Prerequisites

Knowledge of probability theory and basic calculus Knowledge in the field of efficient algorithms

Course Texts Lecture Notes on Analysis of Algorithms and the books

R. Sedgewick and P. Flajolet. An Introduction to the Analysis of Algorithms. R. Sedgewick and P. Flajolet. Analytic Combinatorics.


Module Coordinator Peter Rossmanith

Credits 8



Degree elective



Self-Study Time (h)

Lecture Analysis of Algorithms

MSSSE-1101201.a/11

0 6 4 120

Exercise Analysis of Algorithms

MSSSE-1101201.b/11

0 2 2 30

Masterexam Analysis of Algorithms

MSSSE-1101201.c/11

8 0 0 0

15

Assessment: Lecture Analysis of Algorithms [MSSSE-1101201.a/11] Title Lecture Analysis of Algorithms

Short Title Lecture Analysis of Algorithms

Semester of Study 1



Degree elective

Assessment: Exercise Analysis of Algorithms [MSSSE-1101201.b/11] Title Exercise Analysis of Algorithms

Short Title Exercise Analysis of Algorithms

Semester of Study 1



Degree elective

Assessment: Masterexam Analysis of Algorithms [MSSSE-1101201.c/11] Title Masterexam Analysis of Algorithms

Short Title Masterexam Analysis of Algorithms

Semester of Study 1



Degree elective

16 Revision: 13.06.2013 02:26:16

Module: Parameterized Algorithms [MSSSE-1101202/11] Module Title Parameterized Algorithms

Short Title Parameterized Algorithms

Semester of Study 1


0

Content Many practical problems turned out to be NP-hard and in the classical view they are therefore ``intractable.''

Parameterized algorithms aim at exploiting that practical instances are not as hard as worst case instances. In this lecture, you will learn about such parameterized algorithms and general techniques for their design. We emphasize those techniques that lead to algorithms that are useful in practice. There are also some techniques that easily show that some problems can indeed be solved by a parameterized algorithm, but the running times will be very high. Finally, there are techniques that show that certain problems probably cannot have parameterized algorithms at all based on a complexity theory for parameterized problems.


Knowledge of the most important parameterized algorithms and techniques for their design Ability to design efficient parameterized algorithms for decision and optimization problems Basic knowledge of parameterized complexity theory and the ability to show that certain

problems probably cannot be solved by parameterized algorithms

Prerequisites Knowledge in Efficient Algorithms

Course Texts

R. Downey and M. Fellows. Parameterized Algorithms. R. Niedermeier. Invitation to Fixed-Parameter Algorithms. J. Flum and M. Grohe. Parameterized Complexity Theory.



Credits 8



Degree elective



Self-Study Time (h)

Lecture Parameterized Algorithms

MSSSE-1101202.a/11

0 6 4 120

Exercise Parameterized Algorithms

MSSSE-1101202.b/11

0 2 2 30

Masterexam Parameterized Algorithms

MSSSE-1101202.c/11

8 0 0 0

17

Assessment: Lecture Parameterized Algorithms [MSSSE-1101202.a/11] Title Lecture Parameterized Algorithms

Short Title Lecture Parameterized Algorithms

Semester of Study 1



Degree elective

Assessment: Exercise Parameterized Algorithms [MSSSE-1101202.b/11] Title Exercise Parameterized Algorithms

Short Title Exercise Parameterized Algorithms

Semester of Study 1



Degree elective

Assessment: Masterexam Parameterized Algorithms [MSSSE-1101202.c/11] Title Masterexam Parameterized Algorithms

Short Title Masterexam Parameterized Algorithms

Semester of Study 1



Degree elective

18 Revision: 13.06.2013 02:26:16

Module: Exact Algorithms [MSSSE-1101203/11] Module Title Exact Algorithms

Short Title Exact Algorithms

Semester of Study 1


0


Content

An introduction into exact algorithms fr NP-hard problems, e.g., Branching Dynamic Programming Inclusion-exclusion Measure & Conquer Subset Convolution


Ability to develop fast exact algorithms for hard problems

Prerequisites Suggested: Efficient Algorithms

Course Texts Aktuelle Verffentlichungen



Credits 8



Degree elective



Self-Study Time (h)

Lecture Exact Algorithms MSSSE-1101203.a/11

0 6 4 120

Exercise Exact Algorithms MSSSE-1101203.b/11

0 2 2 30

Masterexam Exact Algorithms

MSSSE-1101203.c/11

8 0 0 0

Assessment: Lecture Exact Algorithms [MSSSE-1101203.a/11] Title Lecture Exact Algorithms

Short Title Lecture Exact Algorithms

Semester of Study 1


19


Degree elective

Assessment: Exercise Exact Algorithms [MSSSE-1101203.b/11] Title Exercise Exact Algorithms

Short Title Exercise Exact Algorithms

Semester of Study 1



Degree elective

Assessment: Masterexam Exact Algorithms [MSSSE-1101203.c/11] Title Masterexam Exact Algorithms

Short Title Masterexam Exact Algorithms

Semester of Study 1



Degree elective

20 Revision: 13.06.2013 02:26:16

Module: Methods in Network Analysis [MSSSE-1101301/11] Module Title Methods in Network Analysis

Short Title Methods in Network Analysis

Semester of Study 1


0

Content

Introduction to the analysis of social networks

Computational aspects of centrality measures

Random graph models, power laws

Computational aspects of clustering measures

Cascading Behavior, diffusion of information

Viral dynamics and viral marketing

Rumor spreading


Critical understanding of fundamental modeling assumptions in the anaylsis of social networks

Knowledge of basic measures for clustering and centrality and their computational aspects

Knowledge of simple models for random graphs and their properties

Ability to mathematically model and analyze problems arising in the design of algorithms for social and information networks

Prerequisites Basic knowledge of algorithms, discrete structures and probability theory

Course Texts

D. Easley, J. Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, 2010 U. Brandes, T. Erlebach. Network Analysis. Springer Verlag, 2005 D. Wasserman, K. Faust. Social Network Analysis. Cambridge University Press, 1994


Module Coordinator Martin Hoefer

Credits 6


Self-Study Time (h) 105

21


Degree elective



Self-Study Time (h)

MSSSE-1101301.a/11

0 4 3 75

MSSSE-1101301.b/11

0 2 2 30

MSSSE-1101301.c/11

6 0 0 0

Assessment: [MSSSE-1101301.a/11] Semester of Study 1


Degree elective

Assessment: [MSSSE-1101301.b/11] Semester of Study 1


Degree elective

Assessment: [MSSSE-1101301.c/11] Semester of Study 1


Degree elective

22 Revision: 13.06.2013 02:26:16

Module: Model Cecking [MSSSE-1102101/11] Module Title Model Cecking

Short Title Model Checking

Semester of Study 1


3

Content Main topics:

Transition systems Concurrent and channel systems Property classes: safety, liveness, invariants, and fairness

Linear Temporal Logic (LTL) Computation Tree Logic (CTL) Model Checking algorithms for LTL and (fair) CTL Abstraction: (Bi)simulation


Acquisition of the following proficiencies:

Modeling of (concurrent) programs Knowledge of property classes Understanding the construction and functioning of model-checking algorithms for LTL and

CTL Understanding of elementary abstraction mechanisms Capability of employing Model Checkers (Spin)

Prerequisites

Knowledge of fundamental automata models and regular languages Knowledge of propositional logic Knowledge of basic data structures such as stacks, trees, and graphs and related

algorithms Basic knowledge of complexity theory

Course Texts Folien zur Vorlesung sowie folgende Lehrbcher: C. Baier, J.-P. Katoen: Principles of Model Checking, MIT Press, 2008. M. Huth and M.D. Ryan: Logic in Computer Science, Modelling and Reasoning about

Systems, Cambridge Univ. Press, 2004. E.M. Clarke, O. Grumberg, D. Peled: Model Checking, MIT Press, 1999.


Module Coordinator Joost-Pieter Katoen Wolfgang Thomas

Credits 6



23


Degree elective



Self-Study Time (h)

Lecture Model Checking MSSSE-1102101.a/11

0 4 3 75

Exercise Model Checking MSSSE-1102101.b/11

0 2 2 30

Masterexam Model Checking

MSSSE-1102101.c/11

6 0 0 0

Assessment: Lecture Model Checking [MSSSE-1102101.a/11] Title Lecture Model Checking

Short Title Lecture Model Checking

Semester of Study 1

Content see moduledescription


Degree elective

Assessment: Exercise Model Checking [MSSSE-1102101.b/11] Title Exercise Model Checking

Short Title Exercise Model Checking

Semester of Study 1



Degree elective

Assessment: Masterexam Model Checking [MSSSE-1102101.c/11] Title Masterexam Model Checking

Short Title Masterexam Model Checking

Semester of Study 1



Degree elective

24 Revision: 13.06.2013 02:26:16

Module: Compiler Construction [MSSSE-1102102/11] Module Title Compiler Construction

Short Title Compiler Construction

Semester of Study 1


3


Lexical analysis of programs (scanner) Syntactic analysis of programs (parser) Semantic analysis of programs (attribute grammars) Generation of optimization of intermediate code Tools for compiler construction (lex, yacc)



Understanding the construction and functioning of compilers for higher-level programming languages Knowledge of using formal methods for syntax specification (regular expressions,

context-free and attribute grammars, EBNF) Capability of implementing simple compiler components (scanner, parser) Knowledge of using compiler-generating tools

Prerequisites

Understanding essential concepts of imperative and object-oriented programming languages and elementary programming techniques Knowledge of basic data structures such as lists, stacks, queues, and trees Knowledge of fundamental automata models such as finite and pushdown automata

Course Texts Folien und Skripte zur Vorlesung sowie folgende Lehrbcher:

A. Aho, R. Sethi, J. Ullman: Compilers -- Principles, Techniques, and Tools. Addison-Wesley, 1988. A.W. Appel, J. Palsberg: Modern Compiler Implementation in Java. Cambridge University

Press, 2002. D. Grune, H.E. Bal, C.J.H. Jacobs, K.G. Langendoen: Modern Compiler Design. Wiley &

Sons, 2000.

Language of Instruction Deutsch

Module Coordinator Thomas Noll Uwe Naumann

Credits 6



Degree elective

25



Self-Study Time (h)

Lecture Compiler Construction

MSSSE-1102102.a/11

0 4 3 75

Exercise Compiler Construction

MSSSE-1102102.b/11

0 2 2 30

Masterexam Compiler Construction

MSSSE-1102102.c/11

6 0 0 0

Assessment: Lecture Compiler Construction [MSSSE-1102102.a/11] Title Lecture Compiler Construction

Short Title Lecture Compiler Construction

Semester of Study 1



Degree elective

Assessment: Exercise Compiler Construction [MSSSE-1102102.b/11] Title Exercise Compiler Construction

Short Title Exercise Compiler Construction

Semester of Study 1



Degree elective

Assessment: Masterexam Compiler Construction [MSSSE-1102102.c/11] Title Masterexam Compiler Construction

Short Title Masterexam Compiler Construction

Semester of Study 1



Degree elective

26 Revision: 13.06.2013 02:26:16

Module: Advanced Model Checking [MSSSE-1102103/11] Module Title Advanced Model Checking

Short Title Advanced Model Checking

Semester of Study 1


4


Abstraction: stutter (bi)simulation Partial-order reduction Binary Decision Diagrams Timed Automata Markov Chains and Decision Processes Timed and Probabilistic CTL Model Checking Probabilistic Processes



Fundamental knowledge of formal models for real-time systems Fundamental knowledge of quantitative extensions of CTL Understanding the functioning of Model Checking algorithms for Timed and Probabilistic

CTL

Prerequisites

Knowledge of elementary probability theory Fundamental knowledge of Model Checking techniques

Course Texts Folien zur Vorlesung sowie folgende Lehrbcher:

C. Baier, J.-P. Katoen: Principles of Model Checking, MIT Press, 2008. J. Rutten, M. Kwiatkowska, G. Norman and D. Parker: Mathematical Techniques for

Analyzing Concurrent and Probabilistic Systems, Volume 23 of CRM Monograph Series. American Mathematical Society, P. Panangaden and F. van Breugel (eds.), March 2004. M. Huth and M.D. Ryan: Logic in Computer Science -- Modelling and Reasoning about

Systems, Cambridge University Press, 2nd edition, 2004 E.M. Clarke, O. Grumberg, D.A. Peled: Model Checking, MIT Press, 1999 K.L. McMillan: Symbolic Model Checking, Kluwer Academic, 1993


Module Coordinator Joost-Pieter Katoen

Credits 6



27


Degree elective



Self-Study Time (h)

Lecture Advanced Model Checking

MSSSE-1102103.a/11

0 4 3 75

Exercise Advanced Model Checking

MSSSE-1102103.b/11

0 2 2 30

Masterexam Advanced Model Checking

MSSSE-1102103.c/11

6 0 0 0

Assessment: Lecture Advanced Model Checking [MSSSE-1102103.a/11] Title Lecture Advanced Model Checking

Short Title Lecture Advanced Model Checking

Semester of Study 1



Degree elective

Assessment: Exercise Advanced Model Checking [MSSSE-1102103.b/11] Title Exercise Advanced Model Checking

Short Title Exercise Advanced Model Checking

Semester of Study 1



Degree elective

Assessment: Masterexam Advanced Model Checking [MSSSE-1102103.c/11] Title Masterexam Advanced Model Checking

Short Title Masterexam Advanced Model Checking

Semester of Study 1



Degree elective

28 Revision: 13.06.2013 02:26:16

Module: Semantics and Verification of Software [MSSSE-1102104/11] Module Title Semantics and Verification of Software

Short Title Semantics and Verification of Software

Semester of Study 1


0

Content

Introduction of WHILE model language Operational, denotational, and axiomatic semantics of WHILE Equivalence of operational and denotational semantics Dataflow analysis and abstract interpretation Abstraction and refinement



Understanding the fundamental concepts of formal semantics for imperative programming languages Capability of reasoning using formal derivation and proof systems Knowledge of basic techniques for program analysis Capability of applying formal concepts for proving compiler correctness Knowledge of using program analysis tools

Prerequisites

Understanding essential concepts of imperative and object-oriented programming languages and elementary programming techniques Knowledge of foundations of formal systems and automata theory Fundamental knowledge of mathematical logic


G. Winskel: The Formal Semantics of Programming Languages. MIT Press, 1993. F. Nielson, H.R. Nielson, C. Hankin: Principles of Program Analysis, 2nd ed., Springer,

2005. H.R. Nielson, F. Nielson: Semantics with Applications: A Formal Introduction, Wiley, 1992.

Language of Instruction Deutsch/Englisch

Module Coordinator Thomas Noll

Credits 6



Degree elective

Assessment and Qualifications

29

Title Ref. Code Credits Credits Workload

Contact hours (h)

Self-Study Time (h)

Lecture Semantics and Verification of Software

MSSSE-1102104.a/11

0 4 3 75

Exercise Semantics and Verification of Software

MSSSE-1102104.b/11

0 2 2 30

Masterexam Semantics and Verification of Software

MSSSE-1102104.c/11

6 0 0 0

Assessment: Lecture Semantics and Verification of Software [MSSSE-1102104.a/11] Title Lecture Semantics and Verification of Software

Short Title Lecture Semantics and Verification of Software

Semester of Study 1



Degree elective

Assessment: Exercise Semantics and Verification of Software [MSSSE-1102104.b/11] Title Exercise Semantics and Verification of Software

Short Title Exercise Semantics and Verification of Software

Semester of Study 1



Degree elective

Assessment: Masterexam Semantics and Verification of Software [MSSSE-1102104.c/11] Title Masterexam Semantics and Verification of Software

Short Title Masterexam Semantics and Verification of Software

Semester of Study 1



Degree elective

30 Revision: 13.06.2013 02:26:16

Module: Modeling Concurrent and Probabilistic Systems [MSSSE-1102105/11] Module Title Modeling Concurrent and Probabilistic Systems

Short Title Modeling Concurrent and Probabilistic Systems

Semester of Study 1


0


Milner's Calculus of Communicating Systems (CCS) and its Semantics Equivalence of CCS Processes Case Study: the Alternating-Bit Protocol Stochastic Processes Probabilistic Process Algebra and its Semantics Equivalence of Probabilistic Processes Probabilities and Nondeterminism Markovian Process Algebra



Formal methods for modeling concurrent systems Fundamentals of Markov Chains Fundamentals of process algebras Understanding of probabilistiv process algebras Knowledge of definition and applications of equivalences for reducing state spaces

Prerequisites

Knowledge of fundamental automata models such as finite and pushdown automata Knowledge of elementary probability theory


R. Milner: Communicating and Mobile Systems: the pi-Calculus. Cambridge University Press, 1999 R. Milner: Communication and Concurrency. Prentice Hall, 1989

H.C. Tijms: A first course in stochastic models. Wiley, 2003 J. Bergstra, A. Ponse, S.A. Smolka: Handbook of Process Algebra. Elsevier, 2001

(Chapters 6 and 11) J. Hillston: A Compositional Approach to Performance Modelling. Cambridge University

Press, 1996 H. Hermanns: Interactive Markov Chains: The Quest for Quantified Quality. LNCS 2428,

Springer 2002 E. Brinksma, H. Hermanns, J.-P. Katoen: Lectures on Formal Methods and Performance

Analysis. LNCS 2090, Springer 2001



31

Thomas Noll

Credits 6



Degree elective



Self-Study Time (h)

Lecture Modeling Concurrent and Probabilistic Systems

MSSSE-1102105.a/11

0 4 3 75

Exercise Modeling Concurrent and Probabilistic Systems

MSSSE-1102105.b/11

0 2 2 30

Masterexam Modeling Concurrent and Probabilistic Systems

MSSSE-1102105.c/11

6 0 0 0

Assessment: Lecture Modeling Concurrent and Probabilistic Systems [MSSSE-1102105.a/11] Title Lecture Modeling Concurrent and Probabilistic Systems

Short Title Lecture Modeling Concurrent, Probabilistic Systems

Semester of Study 1



Degree elective

Assessment: Exercise Modeling Concurrent and Probabilistic Systems [MSSSE-1102105.b/11] Title Exercise Modeling Concurrent and Probabilistic Systems

Short Title Exercise Modeling Concurrent , Probabilistic Syst.

Semester of Study 1



Degree elective

Assessment: Masterexam Modeling Concurrent and Probabilistic Systems [MSSSE-1102105.c/11] Title Masterexam Modeling Concurrent and Probabilistic Systems

Short Title Masteream Modeling Concurrent, Probabilistic Syst.

Semester of Study 1



Degree elective

32 Revision: 13.06.2013 02:26:16

Module: Foundations of the UML [MSSSE-1102106/11] Module Title Foundations of the UML

Short Title Foundations of the UML

Semester of Study 1


0


Sequence diagrams and their semantics Elementary properties of sequence diagrams High-level sequence graphs Communicating finite automata Realizability Statecharts and their semantics Object Constraint Language (OCL) and its semantics



Fundamental knowledge of UML diagrams Understanding of formal semantics of sequence diagrams and Statecharts Knowledge of the Object Constraint Language Capability of applying formal modelling techniques to software systems

Prerequisites

Knowledge of fundamental automata models such as finite and pushdown automata Fundamental knowledge of mathematical logic Knowledge of discrete mathematics Basic knowledge of complexity theory


Jos Warmer and Anneke Kleppe, Object Constraint Language, The: Precise Modeling with UML. Addison Wesley, 2001. D. Harel and M. Politi, Modeling Reactive Systems with Statecharts: The STATEMATE

Approach, McGraw-Hill, 1998.



Credits 6



Degree elective

33



Self-Study Time (h)

Lecture Foundations of the UML

MSSSE-1102106.a/11

0 4 3 75

Exercie Foundations of the UML

MSSSE-1102106.b/11

0 2 2 30

Masterexam Foundations of the UML

MSSSE-1102106.c/11

6 0 0 0

Assessment: Lecture Foundations of the UML [MSSSE-1102106.a/11] Title Lecture Foundations of the UML

Short Title Lecture Foundations of the UML

Semester of Study 1

Content see moduledecsription


Degree elective

Assessment: Exercie Foundations of the UML [MSSSE-1102106.b/11] Title Exercie Foundations of the UML

Short Title Exercise Foundations of the UML

Semester of Study 1



Degree elective

Assessment: Masterexam Foundations of the UML [MSSSE-1102106.c/11] Title Masterexam Foundations of the UML

Short Title Masterexam Foundations of the UML

Semester of Study 1



Degree elective

34 Revision: 13.06.2013 02:26:16

Module: Testing of Reactive Systems [MSSSE-1102107/11] Module Title Testing of Reactive Systems

Short Title Testing of Reactive Systems

Semester of Study 1


0


Groundwork: automata, labelled transitions systems, specification of processes Observation of processes Conformance of processes Derivation of test cases from transition systems Incorporating the quantitative notion of time into test cases Symbolic testing



Basic knowledge of how to describe behaviour, and how to distinguish it by observation In-depth knowledge of the prevalent theories for specification-based testing, in particular

for functional and timed testing Proficiency in proving simple theorems in the context of the lecture

Prerequisites Basic knowledge of finite automata theory

Course Texts Skript Testing of Reactive Systems---Course Notes, on-line erhltlich. Folgende Lehrbcher als ergnzende Literatur:

Manfred Broy, Bengt Jonsson, Joost-Pieter Katoen, Martin Leucker, Alexander Pretschner: Model-Based Testing of Reactive Systems (Advanced Lectures), Volume 3472 of Lecture Notes in Computer Science. Springer-Verlag, 2005


Module Coordinator Henrik Bohnenkamp Joost-Pieter Katoen

Credits 6



Degree elective



Self-Study Time (h)

Lecture Testing of Reactive Systems

MSSSE-1102107.a/11

0 4 3 75

Exercise Testing of Reactive Systems

MSSSE-1102107.b/11

0 2 1 45

35

Masterexam Testing of Reactive Systems

MSSSE-1102107.c/11

6 0 0 0

Assessment: Lecture Testing of Reactive Systems [MSSSE-1102107.a/11] Title Lecture Testing of Reactive Systems

Short Title Lecture Testing of Reactive Systems

Semester of Study 1



Degree elective

Assessment: Exercise Testing of Reactive Systems [MSSSE-1102107.b/11] Title Exercise Testing of Reactive Systems

Short Title Exercise Testing of Reactive Systems

Semester of Study 1



Degree elective

Assessment: Masterexam Testing of Reactive Systems [MSSSE-1102107.c/11] Title Masterexam Testing of Reactive Systems

Short Title Masterexam Testing of Reactive Systems

Semester of Study 1



Degree elective

36 Revision: 13.06.2013 02:26:16

Module: Static Program Analysis [MSSSE-1102109/11] Module Title Static Program Analysis

Short Title Static Program Analysis

Semester of Study 1


0

Course Texts

Flemming Nielson, Hanne R. Nielson, Chris Hankin: Principles of Program Analysis. 2. Ausgabe, Springer, 2005

Helmut Seidl, Reinhard Wilhelm, Sebastian Hack: Ubersetzerbau 3: Analyse und Transformation. Springer, 2009

Steven S. Muchnick, Neil D. Jones: Program Flow Analysis: Theory and Applications. Prentice Hall, 1981


Module Coordinator Thomas Noll

Credits 6



Degree elective



Self-Study Time (h)

Lecture Static Program Analysis

MSSSE-1102109.a/11

0 4 3 75

Exercise Static Program Analysis

MSSSE-1102109.b/11

0 2 2 30

Masterexam Static Program Analysis

MSSSE-1102109.c/11

6 0 0 0

Assessment: Lecture Static Program Analysis [MSSSE-1102109.a/11] Title Lecture Static Program Analysis

Short Title Lecture Static Program Analysis

Semester of Study 1


Degree elective

Assessment: Exercise Static Program Analysis [MSSSE-1102109.b/11] Title Exercise Static Program Analysis

Semester of Study 1


Degree elective

37

Assessment: Masterexam Static Program Analysis [MSSSE-1102109.c/11] Title Masterexam Static Program Analysis

Short Title Masterexam Static Program Analysis

Semester of Study 1


Degree elective

38 Revision: 13.06.2013 02:26:16

Module: Concurrency Theory [MSSSE-1102110/11] Module Title Concurrency Theory

Short Title Concurrency Theory

Semester of Study 1


4


1. Introduction 2. The Interleaving Approach 3. The True Concurrency Approach 4. Refinement and Compositionality 5. Case Studies and Tools 6. Extensions


Acquire the knowledge and competences to: understand the foundations of concurrent systems model (and compare) concurrent systems in a rigorous manner

understand the main semantical underpinnings of concurrency

Prerequisites

Knowledge of fundamental automata models (Course Formale Systeme, Automaten und Prozesse) Understanding of the working principles of parallel and distributed systems (Courses

Betriebssysteme und Systemsoftware and Systemprogrammierung)


Luca Aceto, Anna Inglfsdttir, Kim Guldstrand Larsen and Jiri Srba: Reactive Systems: Modelling, Specification and Verification. Cambridge University Press, 2007. Maurice Herlihy and Nir Shavit: The Art of Multiprocessor Programming. Elsevier, 2008. Jan Bergstra, Alban Ponse and Scott Smolka (Eds.): Handbook of Process Algebra.

Elsevier, 2001. Wolfgang Reisig: Understanding Petri Nets: Modeling Techniques, Analysis Methods,

Case Studies. Springer Verlag, 2012.


Module Coordinator Joost-Pieter Katoen Thomas Noll

Credits 6



Degree elective

39



Self-Study Time (h)

Lecture Concurrency Theory MSSSE-1102110.a/11

0 4 3 75

Exercise Concurrency Theory

MSSSE-1102110.b/11

0 2 2 30

Exam Concurrency Theory MSSSE-1102110.c/11

6 0 0 0

Assessment: Lecture Concurrency Theory [MSSSE-1102110.a/11] Title Lecture Concurrency Theory

Short Title Lecture Concurrency Theory

Semester of Study 1



Degree elective

Assessment: Exercise Concurrency Theory [MSSSE-1102110.b/11] Title Exercise Concurrency Theory

Short Title Exercise Concurrency Theory

Semester of Study 1



Degree elective

Assessment: Exam Concurrency Theory [MSSSE-1102110.c/11] Title Exam Concurrency Theory

Short Title Exam Concurrency Theory

Semester of Study 1



Degree elective

40 Revision: 13.06.2013 02:26:16

Module: Term Rewrite Systems [MSSSE-1102201/11] Module Title Term Rewrite Systems

Short Title Term Rewrite Systems

Semester of Study 1


0

Content basics

syntax of equations semantics of equations

term rewriting

equational reasoning congruence closure term rewrite systems

termination of term rewriting

decidability results reduction relations simplification orders and recursive path orders

confluence of term rewriting

local confluence critical pairs

completion of term rewrite systems

Knuth-Bendix completion implicit induction


learning how to use term rewrite techniques in all areas that require symbolic computation with equations

learning how to use term rewrite techniques for the specification, analysis, and verification of programs. In particular, term rewrite techniques can be used to

analyze whether programs are deterministic analyze whether programs terminate analyze whether programs are correct complete programs and specifications that are incomplete

Prerequisites

first basic knowledge on functional programming would be advantageous, but is not required (lecture Programming Concepts) first basic knowledge on predicate logic would beadvantageous, but is not required (lecture

Mathematical Logic)

41

Course Texts Skript und Folien zur Vorlesung sowie z.B. folgende Bcher:

J. Avenhaus: Reduktionssysteme, Springer, 1995. F. Baader, T. Nipkow: Term Rewriting and All That, Cambridge University Press, 1998. R. Bndgen: Termersetzungssysteme, Vieweg, 1998. E. Ohlebusch: Advanced Topics in Term Rewriting, Springer, 2002 Terese: Term Rewriting Systems, Cambridge University Press, 2003.


Module Coordinator Jrgen Giesl

Credits 6



Degree elective



Self-Study Time (h)

Lecture Term Rewrite Systems

MSSSE-1102201.a/11

0 4 3 75

Exercise Term Rewrite Systems

MSSSE-1102201.b/11

0 2 2 30

Masterexam Term Rewrite Systems

MSSSE-1102201.c/11

6 0 0 0

Assessment: Lecture Term Rewrite Systems [MSSSE-1102201.a/11] Title Lecture Term Rewrite Systems

Short Title Lecture

Semester of Study 1



Degree elective

Assessment: Exercise Term Rewrite Systems [MSSSE-1102201.b/11] Title Exercise Term Rewrite Systems

Short Title Exercise Term Rewrite Systems

Semester of Study 1



Degree elective

Assessment: Masterexam Term Rewrite Systems [MSSSE-1102201.c/11] Title Masterexam Term Rewrite Systems

Short Title Masterexam Term Rewrite Systems

42 Revision: 13.06.2013 02:26:16

Semester of Study 1



Degree elective

43

Module: Logic Programming [MSSSE-1102202/11] Module Title Logic Programming

Short Title Logic Programming

Semester of Study 1


0

Content basics of predicate logic

unification resolution Horn clauses and SLD-resolution

logic programs

operational and denotational semantics evaluation strategies

the programming language Prolog

negation-as-failure non-logical components of Prolog programming techniques

applications and extensions of logic programming Aims and Learning Outcomes

learning the programming techniques in logic languages knowledge of the concepts and the logical foundations of logic languages

learning how to formally define the semantics of logic programming languages learning how to implement logic languages learning how to use logic languages in different application areas

Prerequisites

basic programming concepts (lecture Programming Concepts) first basic knowledge on logic programming would be advantageous, but is not required

(lecture Programming Concepts) first basic knowledge on predicate logic would be advantageous, but is not required

(lecture Mathematical Logic)


I. Bratko: Prolog Programming for Artificial Intelligence, Addison-Wesley, 2001. W. F. Clocksin, C. S. Mellish: Programming in Prolog, Springer, 2003. T. Frwirth, S. Abdennadher: Essentials of Constraint Programming, Springer, 2003. M. Hanus: Problemlsen mit Prolog, Teubner, 1987. J. W. Lloyd: Foundations of Logic Programming, Springer, 1987. P. H. Schmitt: Theorie der logischen Programmierung, Springer, 1992. L. Sterling, E. Shapiro: The art of Prolog, MIT Press, 2000.

44 Revision: 13.06.2013 02:26:16



Credits 6



Degree elective



Self-Study Time (h)

Lecture Logic Programming MSSSE-1102202.a/11

0 4 3 75

Exercise Logic Programming

MSSSE-1102202.b/11

0 2 2 30

Masterexam Logic Programming

MSSSE-1102202.c/11

6 0 0 0

Assessment: Lecture Logic Programming [MSSSE-1102202.a/11] Title Lecture Logic Programming

Short Title Lecture Logic Programming

Semester of Study 1



Degree elective

Assessment: Exercise Logic Programming [MSSSE-1102202.b/11] Title Exercise Logic Programming

Short Title Exercise Logic Programming

Semester of Study 1



Degree elective

Assessment: Masterexam Logic Programming [MSSSE-1102202.c/11] Title Masterexam Logic Programming

Short Title Masterexam Logic Programming

Semester of Study 1



Degree elective

45

Module: Functional Programming [MSSSE-1102203/11] Module Title Functional Programming

Short Title Functional Programming

Semester of Study 1


0

Content introduction to the programming language Haskell

syntax of the different language constructs higher-order functions programming with lazy evaluation monads

denotational semantics of functional programs

complete partial orders and fixpoints denotational semantics of Haskell

lambda calculus

syntax and operational semantics of the lambda calculus reducing Haskell to the lambda calculus

type checking and inference Aims and Learning Outcomes

learning the programming techniques in functional languages knowledge of the foundational concepts behind functional languages learning how to formally define the semantics of functional programming languages learning how to implement functional languages learning how to develop type checking techniques for functional languages

Prerequisites

basic programming concepts (lecture Programming Concepts) first basic knowledge on functional programming would be advantageous, but is not

required (lecture Programming Concepts)


R. Bird: Introduction to Functional Programming Using Haskell, Prentice Hall, 1998. G. Hutton: Programming in Haskell, Cambridge University Press, 2007.

B. O'Sullivan, D. Stewart, J. Goerzen: Real World Haskell, O'Reilly, 2008. P. Pepper: Funktionale Programmierung, Springer, 2002. C. Reade: Elements of Functional Programming, Addison-Wesley, 1989. P. Thiemann: Grundlagen der Funktionalen Programmierung, Teubner, 1994.



46 Revision: 13.06.2013 02:26:16

Credits 6



Degree elective



Self-Study Time (h)

Lecture Functional Programming

MSSSE-1102203.a/11

0 4 3 75

Exercise Functional Programming

MSSSE-1102203.b/11

0 2 2 30

Masterexam Functional Programming

MSSSE-1102203.c/11

6 0 0 0

Assessment: Lecture Functional Programming [MSSSE-1102203.a/11] Title Lecture Functional Programming

Short Title Lecture Functional Programming

Semester of Study 1



Degree elective

Assessment: Exercise Functional Programming [MSSSE-1102203.b/11] Title Exercise Functional Programming

Short Title Exercise Functional Programming

Semester of Study 1



Degree elective

Assessment: Masterexam Functional Programming [MSSSE-1102203.c/11] Title Masterexam Functional Programming

Short Title Masterexam Functional Programming

Semester of Study 1



Degree elective

47

Module: Deductive Program Verification [MSSSE-1102204/11] Module Title Deductive Program Verification

Short Title Deductive Program Verification

Semester of Study 1


0

Content basics

many-sorted predicate logic relations syntax and semantics of functional programs

partial correctness

specifications proving correctness by induction

verification techniques for partial correctness

symbolic evaluation automation of induction proofs heuristics applying lemmata

verification techniques for termination analysis

termination proofs with reduction orders termination proofs with dependency pairs


learning how to use automated reasoning techniques for program verification knowledge about automated techniques for automated induction proofs in order to verify

partial correctness of programs knowledge about methods for automated termination analysis of programs learning how to implement and optimize automated program verification techniques learning how to develop heuristics in order to improve the automation of verification

techniques

Prerequisites

first basic knowledge on functional programming would be advantageous, but is not required (lecture Programming Concepts) first basic knowledge on predicate logic would be advantageous, but is not required

(lecture Mathematical Logic)

Course Texts Skript und Folien zur Vorlesung sowie z.B. folgende Literatur:

T. Arts, J. Giesl: Termination of Term Rewriting Using Dependency Pairs, Theoretical Computer Science, 236:133-178, 2000. K. H. Blsius, H.-J. Brckert: Deduktionssysteme, Oldenbourg, 1992.

48 Revision: 13.06.2013 02:26:16

A. Bundy: The Automation of Proof by Mathematical Induction, Handbook of Automated Reasoning, pages 845-911, Elsevier & MIT Press, 2001. C. Walther: Mathematical Induction, Handbook of Logic in Artificial Intelligence and Logic

Programming, Vol. 2, pages 127-227, Oxford University Press, 1994. C. Walther: Semantik und Programmverifikation, Teubner-Wiley, 2001.



Credits 6



Degree elective



Self-Study Time (h)

Lecture Deductive Program Verification

MSSSE-1102204.a/11

0 4 3 75

Exercise Deductive Program Verification

MSSSE-1102204.b/11

0 2 2 30

Masterexam Deductive Program Verification

MSSSE-1102204.c/11

6 0 0 0

Assessment: Lecture Deductive Program Verification [MSSSE-1102204.a/11] Title Lecture Deductive Program Verification

Short Title Lecture Deductive Program Verification

Semester of Study 1



Degree elective

Assessment: Exercise Deductive Program Verification [MSSSE-1102204.b/11] Title Exercise Deductive Program Verification

Short Title Exercise Deductive Program Verification

Semester of Study 2



Degree elective

Assessment: Masterexam Deductive Program Verification [MSSSE-1102204.c/11] Title Masterexam Deductive Program Verification

Short Title Masterexam Deductive Program Verification

Semester of Study 1


49


Degree elective

50 Revision: 13.06.2013 02:26:16

Module: Modeling and analysis of hybrid systems [MSSSE-1102301/11] Module Title Modeling and analysis of hybrid systems

Short Title Modeling and analysis of hybrid systems

Semester of Study 1


2

Content Hybrid systems are systems with mixed discrete-continuous behaviour. They are everywhere. Physical systems with a discrete part in their control, like automobiles, aircrafts, and other transport systems, robots etc. are hybrid systems. But also software whose real-time behaviour is relevant can be seen as a hybrid system. Such systems play an important role in, e.g., CAD (Computer Aided Design), real-time software, robotics, process control, and computer-aided verification.

In the last years we can observe an intensive development in this area. New methodologies were developed to model such kind of systems and to analyse their behaviour. In this lecture we follow this development and deal with different aspects of hybrid systems, from their modeling to their verification.

Contents:

Discrete, continuous, and dynamic systems, hybrid systems, examples Modeling: Hybrid Automata Some important features: Determinism, blocking systems, Zeno-behaviour, stability

etc. Interesting classes of hybrid systems: Timed Automata, linear systems, non-linear

systems Analysis: Model Checking, deduction, abstraktion, simulation, testing Controller synthesis


The lecture should teach the students how to model, specify, implement, and analyse real-time software systems or discrete controller for continuous systems.

Prerequisites None

Course Texts Wird in der Vorlesung bekannt gegeben.


Module Coordinator Erika Abraham

Credits 6



Degree elective



Self-Study Time (h)

Lecture Modeling and analysis of hybrid systems

MSSSE-1102301.a/11

0 4 3 75

Exercise Modeling and analysis of hybrid systems

MSSSE-1102301.b/11

0 2 1 45

51

Masterexam Modeling and analysis of hybrid systems

MSSSE-1102301.c/11

6 0 0 0

Assessment: Lecture Modeling and analysis of hybrid systems [MSSSE-1102301.a/11] Title Lecture Modeling and analysis of hybrid systems

Semester of Study 1



Degree elective

Assessment: Exercise Modeling and analysis of hybrid systems [MSSSE-1102301.b/11] Title Exercise Modeling and analysis of hybrid systems

Short Title Exercise Modeling and analysis of hybrid systems

Semester of Study 1



Degree elective

Assessment: Masterexam Modeling and analysis of hybrid systems [MSSSE-1102301.c/11] Title Masterexam Modeling and analysis of hybrid systems

Short Title Masterexam Modeling and analysis of hybrid systems

Semester of Study 1



Degree elective

52 Revision: 13.06.2013 02:26:16

Module: Satisfiability Checking [MSSSE-1102302/11] Module Title Satisfiability Checking

Short Title Satisfiability Checking

Semester of Study 1


2

Content

Propositional logic, the satisfiability problem, satisfiability check (SAT-solving) Propositional logic with quantifiers (QBF-solving) First-order logic, theories Theory of equalities and uninterpreted functions, satisfiability check Theory of the reals with addition, satisfiability check (the Simplex method, the Branch and

Bound method, Fourier-Motzkin variable elimination) SAT-solving + Theory-solving: Satisfiability modulo theories (SMT-solving) Deduction, theorem proving Approximative methods Application: Bounded model checking (transition systems, expressing bounded

reachability, expressing safety and lifeness properties)


The students should be able to formalize certain problems in an adequate logic/theory, and check the satisfiability of the resulting formula with the help of adequate algorithms. This way they can decide if the problem is solvable, and eventually determine a satisfying solution.

The following skills are attained: Problem formalization, application of satisfiability checking algorithms, especially for verification purposes.

Prerequisites As regarding contents, the following moduls are needed: Mathematical logic, as well as Algorithms and data structures.

Course Texts Folien der Vorlesung und die folgenden Bcher:

Daniel Kroening, Ofer Strichman: Decision Procedures: An Algorithmic Point of View. Springer Berlin, 2008 Aaron R. Bradley, Zohar Manna: The Calculus of Computation: Decision Procedures with

Applications to Verification. Springer, Berlin. 2007

Language of Instruction Deutsch oder Englisch

Module Coordinator Erika Abraham

Credits 6



53


Degree elective



Self-Study Time (h)

Lecture Satisfiability Checking

MSSSE-1102302.a/11

0 4.5 3 90

Exercises Satisfiability Checking

MSSSE-1102302.b/11

0 1.5 1 30

Masterexam Satisfiability Checking

MSSSE-1102302.c/11

6 0 0 0

Assessment: Lecture Satisfiability Checking [MSSSE-1102302.a/11] Title Lecture Satisfiability Checking

Short Title Lecture Satisfiability Checking

Semester of Study 5


Degree elective

Assessment: Exercises Satisfiability Checking [MSSSE-1102302.b/11] Title Exercises Satisfiability Checking

Short Title Exercises Satisfiability Checking

Semester of Study 5


Degree elective

Assessment: Masterexam Satisfiability Checking [MSSSE-1102302.c/11] Title Masterexam Satisfiability Checking

Short Title Masterexam Satisfiability Checking

Semester of Study 5


Degree elective

54 Revision: 13.06.2013 02:26:16

Module: Applied Automata Theory [MSSSE-1107101/11] Module Title Applied Automata Theory

Short Title AAT

Semester of Study 1


2

Content

Miminization of automata and bisimulation Learning of regular languages Weighted automata, including probabilistic automata Automata und logic Pushdown systems Undecidable problems in automata theory Petri nets


Clear conception of basic state-based models in computer science Ability to assess models with respect to the fundamental properties of expressiveness and

algorithmic complexity

Prerequisites Courses 'Formal Systems, Automata, Processes', 'Computability and Complexity', 'Logic' of Bachelor Curriculum

Course Texts W. Thomas, Applied Automata Theory, Lecture Notes, RWTH Aachen


Module Coordinator Wolfgang Thomas

Credits 6



Degree elective



Self-Study Time (h)

Lecture Applied Automata Theory

MSSSE-1107101.a/11

0 4 3 75

Exercise Applied Automata Theory

MSSSE-1107101.b/11

0 2 2 30

Masterexam Applied Automata Theory

MSSSE-1107101.c/11

6 0 0 0

Assessment: Lecture Applied Automata Theory [MSSSE-1107101.a/11] Title Lecture Applied Automata Theory

Short Title Lecture Applied Automata Theory

55

Semester of Study 1



Degree elective

Assessment: Exercise Applied Automata Theory [MSSSE-1107101.b/11] Title Exercise Applied Automata Theory

Short Title Exercise Applied Automata Theory

Semester of Study 1



Degree elective

Assessment: Masterexam Applied Automata Theory [MSSSE-1107101.c/11] Title Masterexam Applied Automata Theory

Short Title Mastereaxm Applied Automata Theory

Semester of Study 1



Degree elective

56 Revision: 13.06.2013 02:26:16

Module: Infinite Games [MSSSE-1107102/11] Module Title Infinite Games

Short Title Infinite Games

Semester of Study 1


2

Content

Graph-based games and the associated problem of solution Regular winning conditions for infinite games Solution of reachability games and Bchi games Solution of Muller games and parity games Application to automata on infinite trees Decidability of MSO-logic and other logics over infinite trees Outlook 1: Mean pay-off games Outlook 2: Games on infinite graphs, the Borel hierarchy


Knowledge of infinite games as a model for reactive systems Understanding of the algorithmic content of the theory of infinite games Ability to apply game theoretic concepts and algorithms in logic as well as in the

verification and synthesis of systems

Prerequisites Courses of Theoretical Computer Science of Bachelor Curriculum Course 'Infinite Computations'

Course Texts W. Thomas, Automata and Reactive Systems, Lecture Notes, RWTH Aachen 2003



Credits 6



Degree elective



Self-Study Time (h)

Lecture Infinite Games MSSSE-1107102.a/11

0 4 3 75

Exercise Infinite Games MSSSE-1107102.b/11

0 2 2 30

Masterexam Infinite Games MSSSE-1107102.c/11

6 0 0 0

Assessment: Lecture Infinite Games [MSSSE-1107102.a/11] Title Lecture Infinite Games

57

Short Title Lecture Infinite Games

Semester of Study 1



Degree elective

Assessment: Exercise Infinite Games [MSSSE-1107102.b/11] Title Exercise Infinite Games

Short Title Exercise Infinite Games

Semester of Study 1



Degree elective

Assessment: Masterexam Infinite Games [MSSSE-1107102.c/11] Title Masterexam Infinite Games

Short Title Masterexam Infinite Games

Semester of Study 1



Degree elective

58 Revision: 13.06.2013 02:26:16

Module: Tree Automaton [MSSSE-1107103/11] Module Title Tree Automaton

Short Title Tree Automaton

Semester of Study 1


4


Content

Finite automata on ranked trees: bottom-up und top-down tree automata, closure properties and algorithms, regular expressions and grammars, connection to logic Finite automata on unranked trees: coding by ranked trees, connection to XML schema

languages Sequential automaton models: tree walking automata, XPath Tree transformations


Understanding of the concept of finite automata on branching structures and their applications Ability to apply the automata theoretic view on schema languages for XML documents

Prerequisites Courses 'Formale Systeme, Automaten, Prozesse', 'Berechenbarkeit und Komplexitt', 'Mathematische Logik' of Bachelor Curriculum; Knowledge from the course 'Applied Automata Theory' is helpful but not required.

Course Texts Tree Automata: Techniques and Applications. Comon, Hubert; Dauchet, Max; Gilleron, Remi; Jacquemard, Florent; Lding, Christof and Lugiez, Denis; Tison, Sophie; Tommasi, Marc



Credits 4



Degree elective



Self-Study Time (h)

Lecture Tree Automaton MSSSE-1107103.a/11

0 2.5 2 45

Exercise Tree Automaton MSSSE-1107103.b/11

0 1.5 1 30

Masterexam Tree Automaton

MSSSE-1107103.c/11

4 0 0 0

Assessment: Lecture Tree Automaton [MSSSE-1107103.a/11] Title Lecture Tree Automaton

59

Short Title Lecture Tree Automaton

Semester of Study 1



Degree elective

Assessment: Exercise Tree Automaton [MSSSE-1107103.b/11] Title Exercise Tree Automaton

Short Title Exercise Tree Automaton

Semester of Study 1



Degree elective

Assessment: Masterexam Tree Automaton [MSSSE-1107103.c/11] Title Masterexam Tree Automaton

Short Title Masterexam Tree Automaton

Semester of Study 1



Degree elective

60 Revision: 13.06.2013 02:26:16

Module: Recursion Theory [MSSSE-1107104/11] Module Title Recursion Theory

Short Title Recursion Theory

Semester of Study 1


0

Course Texts

W. Thomas, Rekursionstheorie, Skript, RWTH Aachen 2004. N. Cutland, An Introduction to Recursive Function Theory, Cambridge Univ. Press 1980 H. Rogers, Theory of Recursive Functions and Effective Computability, McGrwa Hill 1967

Language of Instruction Deutsch


Credits 4



Degree elective



Self-Study Time (h)

Lecture Recursion Theory MSSSE-1107104.a/11

0 2.5 2 45

Exercise Recursion Theory MSSSE-1107104.b/11

0 1.5 1 30

Masterexam Recursion Theory

MSSSE-1107104.c/11

4 0 0 0

Assessment: Lecture Recursion Theory [MSSSE-1107104.a/11] Title Lecture Recursion Theory

Short Title Lecture Recursion Theory

Semester of Study 1



Degree elective

Assessment: Exercise Recursion Theory [MSSSE-1107104.b/11] Title Exercise Recursion Theory

Short Title Exercise Recursion Theory

Semester of Study 1


61


Degree elective

Assessment: Masterexam Recursion Theory [MSSSE-1107104.c/11] Title Masterexam Recursion Theory

Short Title Masterexam Recursion Theory

Semester of Study 1



Degree elective

62 Revision: 13.06.2013 02:26:16

Module: Infinite Computations [MSSSE-1107105/11] Module Title Infinite Computations

Short Title Infinite Computations

Semester of Study 1


2

Content Part I: Automata on infinite words 1. Bchi automata and regular omega-languages 2. Deterministic automata on infinite words 3. Classification of sequence properties (safety, recurrence, etc.) Part II: Applications 4. Decidability results on logical systems 5. Automata theoretic approach to model-checking 6. Algorithmic results on linear constraints for real numbers Part III: Outlook 7. Context-free omega-languages 8. The Borel hierarchy


Clear conception of infinite objects in computer science and how algorithmic problems on them can be solved Acquaintance with the fundamentals of automata over infinite objects

Course Texts

W. Thomas, Automata and Reactive Systems, Lecture Notes, RWTH Aachen. D. Perrin, J.E. Pin, Infinite Words, Elsevier 2000.



Credits 6



Degree elective



Self-Study Time (h)

Lecture Infinite Computations

MSSSE-1107105.a/11

0 4 3 75

Exercise Infinite Computations

MSSSE-1107105.b/11

0 2 2 30

Masterexam Infinite Computations

MSSSE-1107105.c/11

6 0 0 0

Assessment: Lecture Infinite Computations [MSSSE-1107105.a/11] Title Lecture Infinite Computations

Short Title Lecture Infinite Computations

63

Semester of Study 1



Degree elective

Assessment: Exercise Infinite Computations [MSSSE-1107105.b/11] Title Exercise Infinite Computations

Short Title Exercise Infinite Computations

Semester of Study 1



Degree elective

Assessment: Masterexam Infinite Computations [MSSSE-1107105.c/11] Title Masterexam Infinite Computations

Short Title Masterexam Infinite Computations

Semester of Study 1



Degree elective

64 Revision: 13.06.2013 02:26:16

Module: Regular and Context-Free Languages: Advanced Results [MSSSE-1107106/11] Module Title Regular and Context-Free Languages: Advanced Results

Short Title RCL: Advanced Results

Semester of Study 1


3

Content Part I: Regular Languages 1. Star-height and the star-height problem 2. Star-free languages, first-order logic and Schtzenberger's Theorem 3. Regular languages and circuit complexity Part II: Context-Free Languages 4. Chomsky-Schtzenberger Theorem 5. Generators of context-free, linear, and one counter-languages 6. Deterministic context-free languages


Insight into the wide applicability of regular and context-free languages Knowledge of different viewpoints on these language classes, their classification

and algorithmic results.

Course Texts

H. Straubing, Finite Automata, Formal Logic, and Circuit Complexity, Birkhuser, Boston 1994. J. Berstel, Transductions and Context-Free Languages, Teubner, Stuttgart

M.A. Harrison, Introduction to Formal Language Theory, Addison-Wesley, Reading, Mass. 1978. W. Thomas, Lecture Notes on regular and context-free languages, RWTH Aachen


Credits 4



Degree elective



Self-Study Time (h)

Lecture Regular and context-free languages: Advanced results

MSSSE-1107106.a/11

0 2.5 2 45

Exercise Regular and context-free languages: Advanced results

MSSSE-1107106.b/11

0 1.5 1 30

Masterexam Regular and context-free languages: Advanced results

MSSSE-1107106.c/11

4 0 0 0

Assessment: Lecture Regular and context-free languages: Advanced results [MSSSE-1107106.a/11]

65

Title Lecture Regular and context-free languages: Advanced results

Short Title Lecture RCL: Advanced Results

Semester of Study 1



Degree elective

Assessment: Exercise Regular and context-free languages: Advanced results [MSSSE-1107106.b/11] Title Exercise Regular and context-free languages: Advanced results

Short Title Exercise RCL: Advanced Results

Semester of Study 1



Degree elective

Assessment: Masterexam Regular and context-free languages: Advanced results [MSSSE-1107106.c/11] Title Masterexam Regular and context-free languages: Advanced results

Short Title Masterexam RCL: Advanced Results

Semester of Study 1



Degree elective

66 Revision: 13.06.2013 02:26:16

Module: [MSSSE-1107201/11] Semester of Study 1


0

Course Texts

Folien zur Vorlesung sowie folgende Lehrbcher: Kearns, Vazirani: An Introduction to Computational Learning Theory, MIT Press, 1994

Fischer: Algorithmisches Lernen, Teubner, 1999


Module Coordinator Christof Lding

Credits 4



Degree elective



Self-Study Time (h)

MSSSE-1107201.a/11

0 3 2 60

MSSSE-1107201.b/11

0 1 1 15

MSSSE-1107201.c/11

4 0 0 0

Assessment: [MSSSE-1107201.a/11] Semester of Study 1


Degree elective

Assessment: [MSSSE-1107201.b/11] Semester of Study 1


Degree elective

Assessment: [MSSSE-1107201.c/11] Semester of Study 1


Degree elective

67

Module: Complexity theory and quantum computing [MSSSE-1107301/11] Module Title Complexity theory and quantum computing

Short Title Complexity theory and quantum computing

Semester of Study 1


0


Content Deterministic, non-deterministic, parallel and probabilistic models of computations and associated complexity classes, complete problems, introduction to the mathematical and physical foundations of quantum computing, quantum bits and quantum registers, quantum gate arrays, important quantum algorithms, especially Shor's factorisation algorithm


The students shall be enabled to classify problems according to their complexity. They shall become acquainted with the most important complexity classes for deterministic, non-deterministic, parallel and probabilistic models of computation and their relationship. Furthermore, the students shall become proficient in the foundations and important algorithms in quantum computing.

Prerequisites Successful completion of modules Mathematical Foundations, Linear

Module English

Documents

Transcript of Module English