VBM673 - DISTRIBUTED SOFTWARE DEVELOPMENT

Course Name Code Semester Theory
(hours/week)
Application
(hours/week)
Credit ECTS
DISTRIBUTED SOFTWARE DEVELOPMENT VBM673 Any Semester/Year 3 0 3 6
Prequisites-
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Problem Solving
Project Design/Management
 
Instructor (s)Yrd. Doç. Dr. Kayhan Ä°mre 
Course objectiveThe aim of this course is to provide students hands-on experience about the distributed systems. 
Learning outcomes
  1. ? get information on software architectures.
  2. ? get information on distributed software development tools.
  3. ? develop distributed application.
Course Content? Basics of parallel programming
? Software architectures
? Software segmentation
? Distributed object programming
? Tools for distributed programming
 
References? Qing Wang, Dietmar Pfahl, David Raffo, Making Globally Distributed Software Development a Success Story, Springer, 2008
? Sol M. Shatz, Development of Distributed Software: Concepts and Tools, Macmillan Coll, 2003
 

Course outline weekly

WeeksTopics
Week 1Introduction
Week 2Parallel computers
Week 3Parallel backup
Week 4Data organization
Week 5Parallel algorithms
Week 6Midterm I
Week 7Parallelism and data process
Week 8Parallel programming
Week 9Software architectures
Week 10Software segmentation
Week 11Distributed object programming
Week 12Tools for distributed programming
Week 13Case Study
Week 14Presentation of final projects
Week 15Presentation of final projects (cont?d.)
Week 16Final exam

Assesment methods

Course activitiesNumberPercentage
Attendance00
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments00
Presentation00
Project130
Seminar00
Midterms130
Final exam140
Total100
Percentage of semester activities contributing grade succes260
Percentage of final exam contributing grade succes140
Total100

WORKLOAD AND ECTS CALCULATION

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)14228
Presentation / Seminar Preparation000
Project13333
Homework assignment000
Midterms (Study duration)13333
Final Exam (Study duration) 14444
Total Workload31115180

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Has detailed knowledge about data and knowledge engineering (DKE). X   
2. Has a good understanding of common concepts such as abstraction, complexity, security, concurrency, software lifecycle and applies their expertise to the effective design, development and management of IS.  X  
3. Understands the interaction of theory and practice and the links between them.  X  
4. Has the ability to think at different levels of abstraction and detail; understands that an IS can be considered in different contexts, going beyond narrowly identifying implementation issues.   X 
5. Solves any technical or scientific problem independently and presents the best possible solution; has the communication skills to clearly explain the completeness and assumptions of their solution.    X
6. Completes a project on a larger scale than an ordinary course project in order to acquire the skills necessary to work efficiently in a team.    X
7. Recognises that the field of DKE is rapidly evolving. Follows the latest developments, learns and develops skills throughout their career.   X 
8. Recognises the social, legal, ethical and cultural issues related to DKE practice and conduct professional activities in accordance with these issues. X   
9. Can make oral presentations in English and Turkish to different audiences face-to-face, in writing or electronically.    X
10. Recognises that DKE has a wide range of applications and opportunities.   X 
11. Is aware that DKE interacts with different fields, can communicate with experts from different fields and can learn necessary field knowledge from them.   X 
12. Define a research problem and use scientific methods to solve it.   X  

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest