  Nowadays, we are witnessing an increasing adoption of Artificial Intelligence
(AI) to develop techniques aimed at improving the reliability, effectiveness,
and overall quality of software systems. Deep reinforcement learning (DRL) has
recently been successfully used for automation in complex tasks such as game
testing and solving the job-shop scheduling problem. However, these specialized
DRL agents, trained from scratch on specific tasks, suffer from a lack of
generalizability to other tasks and they need substantial time to be developed
and re-trained effectively. Recently, DRL researchers have begun to develop
generalist agents, able to learn a policy from various environments and capable
of achieving performances similar to or better than specialist agents in new
tasks. In the Natural Language Processing or Computer Vision domain, these
generalist agents are showing promising adaptation capabilities to
never-before-seen tasks after a light fine-tuning phase and achieving high
performance. This paper investigates the potential of generalist agents for
solving SE tasks. Specifically, we conduct an empirical study aimed at
assessing the performance of two generalist agents on two important SE tasks:
the detection of bugs in games (for two games) and the minimization of makespan
in a scheduling task, to solve the job-shop scheduling problem (for two
instances). Our results show that the generalist agents outperform the
specialist agents with very little effort for fine-tuning, achieving a 20%
reduction of the makespan over specialized agent performance on task-based
scheduling. In the context of game testing, some generalist agent
configurations detect 85% more bugs than the specialist agents. Building on our
analysis, we provide recommendations for researchers and practitioners looking
to select generalist agents for SE tasks, to ensure that they perform
effectively.
