Software Engineering Fundamentals

7.5 credits

Syllabus, Bachelor's level, 2IS232

Code
2IS232
Education cycle
First cycle
Main field(s) of study and in-depth level
Information Systems G1N, Software Engineering G1N
Grading system
Pass with distinction (VG), Pass (G), Fail (U)
Finalised by
The Department Board, 12 June 2025
Responsible department
Department of Informatics and Media

Entry requirements

General entry requirements and Mathematics Further level 1b or level 1c, Social Studies level 1b or level 1a2, English level 2, or Mathematics 3b or 3c/Mathematics C, Social Studies 1b or 1a1+1a2, English 6

Learning outcomes

Regarding knowledge and understanding the student is expected to be able to on completion of the course:

  • explain the architecture and function of computer systems,
  • account for digital representation of information,
  • describe notations such as pseudocode and flowcharts.

Regarding competence and skills the student is expected to be able to on completion of the course:

  • carry out common arithmetic and logical operations on binary, octal and hexadecimal numbers,
  • systematically apply problem solving methodology,
  • interpret, describe and model algorithms using notations such as pseudocode and flowcharts
  • describe opportunities and limitations with AI, including generative AI.

Content

The course deals with how computers function as a system of interacting components, as well as gives insight into the function of the microprocessor. Various types of software are discussed. Further, the concepts of high level programming language, compilation and machine code are used to illustrate how software, software development, and execution of machine code in the microprocessor are linked.

Digital representation of information is addressed based on the binary number system. To give a general understanding of positional systems, hexadecimal and octal numbers are also treated to give the students a general understanding of positioning systems. Further, the concept data type, different data types, and related operators are included.

Based on the microprocessor's working method and representation of data students work with different methods to interpret and model algorithms. The work with algorithms is dealt with as part of a general problem solving methodology.

Generative AI is addressed in the perspective of opportunities and limitations with a focus on the user's perspective.

Instruction

The teaching is given as lectures and laboratory work.

Assessment

The course is examined through laboratory work, assignments and written exam.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator or a decision by the department's working group for study matters.

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