Teaching

Im Programmierpraktikum I werden grundlegende Kenntnisse der objektorientierten Programmierung auf Basis der Sprache Java vermittelt. Die Studierenden werden von dieser Sprache vor allem folgende Grundmerkmale und Konzepte kennenlernen:

• Basiskonzepte der Programmierung: einfache Datentypen, Variablen, Operatoren, Anweisungen, Kontrollstruk-turen
• Zusammengesetzte Datentypen (Felder)
• Objektorientierte Programmierung
• Klassen (Attribute, Methoden, Konstruktoren)
• Vererbung
• Pakete, abstrakte Klassen und Interfaces
• Java API und wichtige Hilfsklassen
• Ausnahmebehandlung: Exceptions
• Programmierung Grafischer Oberflächen Die Programmierausbildung erfolgt auf der Basis des Be-triebssystems Linux. Hierzu werden ebenfalls Grundkenntnisse zu Werkzeugen vermittelt, die es ermöglichen, einfache Java-Programme zu entwickeln.

ECTS: 5
Sprache: Deutsch
Vorkenntnisse: Benutzerkenntnisse eines modernen Betriebssystems
Prüfungsleistung: Programmiertestat

Im Programmierpraktikum II werden die erworbenen Kenntnisse aus der Veranstaltung Programmierpraktikum I erweitert und vertieft. Basierend auf der Programmiersprache Java, werde hier die folgenden Themengebiete vermittelt:

• Generische Datentypen,
• Dynamische Mengen (Collections Framework)
• Stream-Klassen (Java IO)
• Client-Server Kommunikation
• Multi-Threading
• JDBC (Datenbanken)
• Verarbeitung von XML-Dokumenten
• Reflection API
• Testen (JUnit)
• Weitere ausgewählte Themen

Darüber hinaus werden Werkzeuge für die Teamorientierte Entwicklung größerer Programmpakete vorgestellt. Dazu gehört insbesondere die Entwicklungsumgebung Eclipse.

ECTS: 5
Sprache: Deutsch
Vorkenntnisse: Programmierpraktikum I
Prüfungsleistung: Programmiertestat

This course teaches students about engineering methods and tools for team-oriented development of non-trivial software systems. In particular:

• Software development processes
• System and requirements analysis
• Application design and system architecture
• Software quality
• Validation, verification and testing
• Maintenance

The students learn about key technologies and processes of modern software engineering. After completion of the course they will be able to describe, design and develop complex software systems while accounting for requirements and risks common in industrial projects (e.g. quality, costs, deadlines, etc).

ECTS: 6
Language: English
Evaluation: written exam
Prerequisites: CS 302 – Praktische Informatik I, CS 307 – Algorithmen und Datenstrukturen, CS 304 – Programmierpraktikum I
Recommended: CS 305 – Programmierpraktikum II

The course will provide data scientists with the knowledge they need to be able to apply Python3 in data science projects.

Topics covered include –

• basic concepts of (object-oriented) programming
• programing in the Python programming language
• advanced libraries such NumPy and pandas
• Python development tools (text editor, IDE)

After taking the course, students will be familiar with Python3 and will be able to use it in data science projects. 

ECTS: 6
Language: English
Prerequisites: None
Evaluation: written exam

Participants: MMSDS students only

The course deals with the model-based specification of software systems and components as well as their verification, validation and quality assurance. The emphasis is on view-based specification methods that use multiple views, expressed in multiple languages, to describe orthogonal aspects of software systems/components. Key examples include structural views represented using class diagrams, operational views expressed using constraint languages and behavioural views expressed using state diagrams. An important focus of the course is the use of these views to define tests and extra-functional properties.

After taking the course, students will be familiar with the latest state-of-the-art techniques for specifying the externally visible properties of a software system/component – that is, for describing a software system/component as a 'black box'. Participants will also know how to use the expertise acquired during the course to describe the requirements that a system/component has to satisfy and to define tests to check whether a system/component fulfils these requirements. With the acquired skills and know-how, students will be able to play a key role in projects involving the development of systems, components and software applications.

ECTS: 6
Language: English
Prerequisites: None
Evaluation: written exam

The course focuses on the principles, practices and tools involved in advanced model driven development. This includes established modeling standard languages (e. g. UML, ATL, OCL, etc. ) and modeling infrastructures (e.g. MOF, EMF, etc. ) as well as bleeding edge, state-of-the-art modeling technologies (e. g. LML, PLM, etc.). Key topics addressed include:

• Multi-level modeling
• Meta-modeling
• Ontology engineering versus model engineering
• Model transformations
• Domain specific language definition and use
• Model creation and evolution best practices
• Model driven software development
• Model checking and ontology validation

After taking the course students will be familiar with the accepted best practices and technologies used in mainstream model-driven development as well as state-of-the-art modeling technologies emerging from research institutions. Students will also know how to apply modeling technologies in real-world projects and will have the capability to analyse, understand and model complex systems.

ECTS: 6
Language: English
Prerequisites: CS 500 – Advanced Software Engineering
Evaluation: written exam

The course introduces the fundamental principles, practices and applications of generative software engineering (GSE) as well as the supporting models, tools and services. Key topics addressed include:

• Software Recommendation and Reuse
• Code Search Engines
• Principles of GSE
• Existing GSE models, tools and services
• Defining GSE “problems”
• Applications of GSE
• Evaluation and comparison of GSE models, tools and services

After taking the course students will be familiar with the principles, practices and applications of GSE as well as supporting models, tools and services. Students will have the ability to judge, select, and apply GSE techniques and tools in practical software engineers, as well as the ability to understand academic GSE publications and to perform research in GSE. Students will have improved skills in analytical thinking and applying theoretical knowledge to solve practical problems, especially in the use of AI to enhance software engineering.

ECTS: 6
Language: English
Prerequisites: CS 500 – Advanced Software Engineering (Software Testing and Experimentation)

Recommended: Knowledge of Java and/or Python
Evaluation: written exam

Contact: Marcus Kessel