Course syllabus - Test Automation in the Software lifecycle, 7.5 hp
Information about the course
- Course code: FDAV002
- Third-cycle subject: Computer Science
- School: School of Innovation, Design and Engineering
- Responsible department: NES
- Valid from: 2025-12-19
- Established by: Dean of School
- Decision date: 2025-12-19
- Level of education: Third cycle level
Course objective
The course focuses on the basic and fundamental principles for test automation and verification throughout the software lifecycle, from requirements, architecture, design, implementation and test to release, monitoring and maintenance in different domains for industrial and administrative software systems. Practical aspects like how to build an effective CI/CD environment, find a workflow as well as digitalize aspects of documentation and process are necessary to increase efficiency and effectiveness within the software lifecycle. Basic concepts, like integration of tools, automatic fault fixing, sustainability, as well as different optimizations are important contributors in test automation, where we explore the industry 5.0 perspective. We take our stance in Agile and DevOps processes, with practical industry cases, and we will identify automatic methods, e.g. simple scripts, multi-agents, orchestration, but also autonomous systems, driven with or without AI support.
The role of Generative AI within Test automation is discussed. The course goal aims to give the students a deep understanding on how to drive improvements around automation with focus on test automation, increase automation reliability, speed, quality and digitalization.
Course content
· Automation within the software lifecycle, with emphasis on testing in all phases
- Different processes, ways of working and workflows utilizing automation
- Digitalization and data collection for automation
- How to introduce tools, evaluate them and integrate them in different phases
- Methods for test automation (and quality improvements): Scripting, orchestrations, agents, multi-agents with AI support, Generative AI and autonomous systems
- Automated fault and fix-loops, sustainability (Industry 5.0) and optimizing automation and test in an integrated flow
- To drive improvement projects within Industry for automation and test improvement models
Intented learning outcomes
- Describe and apply the main methods for automation, from scripting, different tools, orchestration, agents, multi-agents with AI support, generative AI and autonomous systems
- Describe test improvement models, tool evaluations, digitalization, as well as data collection for automation
- Analyze industrial software processes and features of digitalization, work flows as well as fault-fix loops
- Conduct an empirical analysis of improvement areas for automation of software lifecycle through using above methods, and suggest practical improvements for test and verification for an improved flow and quality
- Describe the goals with Industry 5.0 and how they can be realized in software development, as well as being able to describe measurements and goals for improvements.
The intended qualitative targets in relation to the Higher Education Ordinance, appendix 2.
Knowledge and understanding
For the Degree of Doctor, the doctoral student shall demonstrate:
- A1: broad knowledge and systematic understanding of the research field as well as advanced and up-to-date specialised knowledge in a limited area of this field,
Competence and skills
For the Degree of Doctor, the doctoral student shall demonstrate
- B1: the capacity for scholarly analysis and synthesis as well as to review and assess new and complex phenomena, issues, and situations autonomously and critically,
- B2: the ability to identify and formulate issues with scholarly precision critically, autonomously, and creatively, and to plan and use appropriate methods to undertake research and other qualified tasks within predetermined time frames and to review and evaluate such work,
- B3: through a dissertation the ability to make a significant contribution to the formation of knowledge through his or her own research,
- B5: the ability to identify the need for further knowledge,
Judgement and approach
For a Degree of Doctor the doctoral student shall demonstrate
- C2: specialised insight into the possibilities and limitations of research, its role in society and the responsibility of the individual for how it is used.
Teaching formats
The course consists of five full-day workshops with lectures and practical exercises. This includes writing an equivalent of a workshop paper. It also includes a full day for presentations/seminars where the results of a small project are presented. Compulsory attendance required for lectures and seminars.
Examination
SEM1: 3.0hp (Learning outcome 1, 2, 5)
GRU1: 3.5hp (Learning outcome 3, 4)
OBN1: 1.0hp (Learning outcome 1, 2, 5)
Grade
Examinations included in the course are assessed according to a two-grade scale, fail or pass.
Grades are to be decided by a teacher specially appointed by the University.
A person who has not passed the regular examination shall be given the opportunity to retake the test.
Requirements
To participate in the course and the examinations included in the course, the applicant must be admitted to doctoral studies. Subject to availability, teachers at Mälardalen University that hold a doctorate can be offered to take part of the course.
Specific entry requirements
- Basic knowledge of program testing is required
Selection criteria
Selection of applicants will be made in accordance with the ranking below.
- Doctoral students in Computer Science at Mälardalen University
- Doctoral students at Mälardalen University
- Doctoral students at other higher education institutions in Sweden