Course syllabus for AI and software engineering seminar

Course syllabus adopted 2026-02-18 by Head of Programme (or corresponding).

Overview

  • Swedish nameAI- och programvarutekniksseminarium
  • CodeDAT550
  • Credits7.5 Credits
  • OwnerMPSOF
  • Education cycleSecond-cycle
  • Main field of studyComputer Science and Engineering, Software Engineering
  • DepartmentCOMPUTER SCIENCE AND ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 24122
  • Maximum participants30 (at least 10% of the seats are reserved for exchange students)
  • Open for exchange studentsYes

Credit distribution

0122 Written and oral assignments 7.5 c
Grading: TH		 7.5 c

In programmes

Examiner

Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements

Course specific prerequisites

To be eligible for the course, the student should have a bachelor¿s degree in Software Engineering, Computer Science, Computer Engineering, Information Technology, Information Systems, or equivalent.

In addition, the student should have completed courses in:
  • A basic course in machine learning (e.g. Introduction to data science and AI, Software Engineering for AI Systems, Software Engineering for Data-Intensive AI Applications, or equivalent)
  • A general Software Engineering course (e.g. Software Engineering: Theory and Practice or equivalent) or 6 credits in one or more of the following areas of software engineering: software processes and agile development, software architecture, software quality assurance or testing, requirements engineering.

Aim

Artificial intelligence and machine learning are more and more used in practice. However, the introduction of AI/ML components into a software system or the software development process comes with new challenges and needs and changes the way the software system is engineered. The course will comprise of a number of themes with respect to software engineering of AI/ML-Enabled Systems and the application of AI within software engineering.

Learning outcomes (after completion of the course the student should be able to)

Knowledge and understanding
  • Explain processes and engineering practices for developing AI/ML-enabled systems, from requirements engineering to testing
  • Explain typical roles in software engineering of AI/ML enabled systems as well as challenges in interdisciplinary teams consisting of Data Scientists and Software Engineers
  • Explain typical requirements for AI/ML components, such as non-functional requirements, requirements on data, and contextual requirements
  • Explain architectures and patterns for AI/ML-enabled systems
  • Describe existing techniques to verify and explain decisions made by AI/ML- enabled systems
  • Explain the use and limitations of AI to automate and augment software engineering workflow
  • Provide an overview of recent research on SE for AI/ML-enabled systems (SE for AI) and AI for software engineering (AI for SE).

Skills and abilities
  • Read and critically analyse research about: 
    (1) The application of software engineering methods and practices to develop reliable AI/ML-enabled systems,
    (2)The use of AI/ML techniques to improve software engineering processes (AI for SE)
  • Present and critically discuss the research design, findings and implication of selected studies in these areas.
Judgement and approach
  • Judge the extent to which an AI/ML component, either AI/ML models embedded within software systems or AI-driven tools used in software engineering,  needs to be safe-guarded
  • Judge what verification methods are appropriate when developing or using an AI/ML- enabled system given the requirements of that system
  • Judge fairness, bias, and potential other ethical issues of an AI/ML-enabled system
  • Judge limitations of a state-of-the-art software engineering approach for AI/ML and AI tools used for software engineering given evidence presented in research papers

Content

The course will comprise a number of themes with respect to software engineering of AI/ML-Enabled Systems and the application of AI within software engineering:
  • Processes, Engineering Practices, and Interdisciplinary Teams
  • Requirements Engineering
  • Architectures
  • Verification and Testing
  • Analysis of Failure Cases and Debugging
  • Fairness, Bias, and Ethics
  • User Management and Explaining AI Decisions

Organisation

The course is provided in the form of a literature seminar, which combines reading papers, student presentations, and discussions. Students will explore one of the topics in detail and gain more summative knowledge in the other topics. An individual report is the final element of the course.

Literature

This course does not have mandatory literature. Students identify relevant research articles during the course through a literature review.

Examination including compulsory elements

The examination consists of an individual presentation. In addition an active participation and contribution to discussions is required as well as an individual report.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.

AI and software engineering seminar | Chalmers