Towards an Excellent Research Environment in Trustworthy GenAI-assisted Software Engineering (Terse)
Terse explores how artificial intelligence is transforming software development and how education, research, and industry can respond to emerging skills needs.
Generative artificial intelligence is transforming software engineering. Tasks that once required extensive manual coding can now be supported by generative AI tools, creating new opportunities while also raising important questions about quality, security, trust, and professional expertise.
Towards an Excellent Research Environment in Trustworthy GenAI-Assisted Software Engineering (Terse) explores how this transformation is reshaping software development. The project investigates how developers use generative AI, how professional roles and work practices are evolving, and which skills will be needed in the future.
Understanding the future of software development
As generative AI becomes an increasingly important part of software engineering, Terse will examine its impact on both people and organisations. The research will explore how developers integrate AI-assisted tools into their work, how responsibilities and workflows change, and how education and training can keep pace with rapidly evolving industry needs.
The project will also address key challenges related to reliability, security, transparency, and trustworthiness, helping ensure that AI is used responsibly in software development
Close collaboration with industry and the public sector
Through close collaboration with industry and public sector organisations, Terse will generate new knowledge about how generative AI is being adopted and integrated into software engineering practice. These partnerships will provide valuable insights into real-world practice while helping to ensure that the research remains relevant to current and future needs.
Building on this knowledge, the project will develop methods, frameworks, and tools that support the reliable and trustworthy use of artificial intelligence in software engineering.
Terse aims to educate the next generation of software developers, strengthen research capacity, and lay the foundation for larger national and international research initiatives focused on the responsible use of AI.
Funded through HVL’s Excellence in research initiative
Terse builds on a strong software engineering research environment at the Western Norway University of Applied Sciences (HVL). The research group has extensive expertise in areas such as model-driven engineering, software testing, software security, and software quality.
The project is funded through HVL's Excellence in Research initiative. This support will strengthen an internationally competitive research environment and help position it for future national and international research collaborations and funding opportunities.
Project leader
The project will be organized into four interlinked work packages
Capacity and network building
The work package will further develop the academic network and foster collaboration with industry partners.
Work package leader
Empirical evidence collection
Working closely with industry partners, the work package will carry out surveys, interviews, case studies, and experiments to investigate current practices and emerging challenges.
Work package leader
Methods and technology development
Work package leader
Education and skills transformation
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Previous and ongoing projects
Publications
- Lima, N.T Nguyen, R. Heldal, L.M. Kristensen, T.D. Oyetoyan, P. Pelliccione, E. Knauss: A Data Flow Oriented Software Architecture for Heterogeneous Marine Data Streams. International Conference on Software Architecture (2024).
- S. de Boer, E.B. Johnsen, V.K.I Pun, S.L.T. Tarifa: Proving Correctness of Parallel Implementations of Transition System Models. ACM Transaction on Prog. Language Systems (2024).
- Barriga, A. Rutle, R. Heldal: AI-powered model repair: an experience report - lessons learned, challenges, and opportunities. Software Systems Modelling (2022).
- Heldal et al.: How do Practitioners Perceive the Relevance of Requirements Engineering Research? IEEE Trans. on Software Engineering (2022).
- Audrito, R. Casadei, F. Damiani, V. Stolz, M. Viroli: Adaptive distributed monitors of spatial properties for cyber-physical systems. Journal of Systems and Software (2021).