on, leading to unfair or discriminatory software applications [172]. These
biases can be perpetuated if appropriate human oversight mechanisms, which
require transparency, are not set in place. For instance, mechanisms to avoid
automation bias are largely needed, especially for LLMs that usually make
confident statements about their decisions. This could lead, in the future, as
stated by the participants in the survey conducted by Cabrero-Daniel et al.,
to a potential loss of human expertise as tasks become automated and GenAI
tools become common-use [210], leading to a diminished ability to address or
understand complex software challenges in the future manually.

Sustainability concerns. As a general concern in sustainable software
engineering [211, 212, 213], the appearance of lots of LLMs daily raise a
concern about their environmental impact, i.e. energy consumption [214, 215,
216]. Chiet et al. showed that ChatGPT-like services, inference dominates
emissions, in one year producing 25 times the CO2 emissions of training GPT-3
[215]. LLM capabilities have been associated with their size [217], resulting
in the rapid growth of model size [218]. While it has been suggested that the
model performance depends not only on model size but also on the volume of
training data [74], the question of a triple-bottom-line model size that at
the same time achieves performance, environmental and organizational
objectives, remains unclear.

##  5 Outlook and Conclusions

The field of GenAI in SE is an important and fast-moving SE research area,
currently presenting opportunities as well as open challenges. GenAI has the
power to redefine the way software is designed, developed, and maintained. The
innovative capabilities of AI-driven systems promise to streamline processes,
boost efficiency, and open up new avenues for creative problem-solving.
However, there are also common challenges in adopting GenAI and specific
challenges for SE activities, i.e., requirements engineering, software
implementation, quality assurance, and SE education. Our research agenda,
outlined in this paper, stands as one of the pioneering initiatives to
systematically reveal the state of research as of Oct 2023. Software
Engineering is a large spectrum that includes technical activities but also
user-centric, process-driven and managerial aspects. A comprehensive view
should consider insights and methodologies from research about software
project development, software project management, professional competencies,
software engineering education, and software businesses. Therefore,
collaboration with experts from these diverse disciplines is essential to
harness the full potential of this research area.

In light of the evolving nature of GenAI research, significant work remains to
establish GenAI in SE as a mature research area. Substantial theories must be
developed to provide a solid theoretical underpinning for the practical
applications. Technological advancements should be validated in a realistic
project context. The adoption of GenAI in SE can also be combined with other
active research areas, e.g., sustainability, trustworthy systems, and
education.

We acknowledge that this research agenda might still miss some important SE
aspects where GenAI can be applied. RQs are presented to illustrate
significant concerns in each SE area and are not meant to be exhaustive. The
research agenda is open to additions of new tracks, topics, and RQs by other
researchers interested in the research area. Through this collective effort
and commitment from a diverse community of researchers, we can strengthen the
foundations of GenAI in SE and ensure its relevance in a rapidly changing
technological landscape. Declaration of Generative AI and AI-assisted
technologies in the writing process During the preparation of this work, we
used ChatGPT version 3.5 and Grammarly services to edit and improve our
writing, i.e. rephrasing sentences and fixing typos and language mistakes.
After using this tool/service, we reviewed and edited the content as needed
and take(s) full responsibility for the content of the publication.

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