Navigation

26D111VIR - Artificial intelligence in Software Development

Course specification
Course title Artificial intelligence in Software Development
Acronym 26D111VIR
Study programme Electrical Engineering and Computing
Module Software Engineering
Type of study doctoral studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
      ESPB 9.0 Status elective
      Condition none
      The goal Introduction to the use of artificial intelligence in software development, in creating specifications, code generation, test automation, software project management, and documentation. Large language models in the educational process related to programming. Software development complexity and reduction of development time and costs using artificial intelligence. Error, risk and failure control.
      The outcome The expected outcome is the ability of the student to analyze the problem, select appropriate tools from the domain of artificial intelligence for software development, and effectively evaluate their application in order to increase the efficiency of software development, as well as the quality of the software product.
      Contents
      URL to the subject page https://teams.microsoft.com/l/team/19%3AHN7LjJV_dmYr5dwyvgIyCYCG6bd0hICd4KvonACcsHw1%40thread.tacv2/conversations?groupId=a7b2ba32-4eef-439c-9c3f-b8fc0f807749&tenantId=1774ef2e-9c62-478a-8d3a-fd2a495547ba
      Contents of lectures Generative AI models in software development. Examples of tools and their applications, e.g. GitHub Copilot, ChatGPT, Code Llama 2. Assessment of the efficiency and quality of generated code and documentation. Analysis of scientific papers, case studies in the form of seminars. Changes in the structure of programming teams and assessments of economic effects; social aspects of the profession.
      Contents of exercises Work on individual project with presentation
      Literature
      1. Chip Huyen, AI Engineering - Building Applications with Foundations Models, O'Reilly, 2024
      2. Michael Kofler, AI-Assisted Coding: A Practical Guide to Boosting Software Development with ChatGPT, GitHub Copilot, Ollama, Aider, and Beyond, Rheinwerk Computing, O'Reilly & Associates, 2025.
      3. Sauvola, Jaakko, et al. "Future of software development with generative AI." Automated Software Engineering, Vol. 31, No. 1, 2024.
      4. Coutinho, Mariana, et al. "The role of generative AI in software development productivity: A pilot case study." Proceedings of the 1st ACM International Conference on AI-Powered Software. 2024.
      5. Barenkamp, Marco, Jonas Rebstadt, and Oliver Thomas. "Applications of AI in classical software engineering." AI Perspectives, Vol. 1, No. 2, Springer, 2020.
      Number of hours per week during the semester/trimester/year
      Lectures Exercises OTC Study and Research Other classes
      8
      Methods of teaching Lectures, individual project
      Knowledge score (maximum points 100)
      Pre obligations Points Final exam Points
      Activites during lectures 0 Test paper 0
      Practical lessons 0 Oral examination 30
      Projects 70
      Colloquia 0
      Seminars 0