  Many software systems today face uncertain operating conditions, such as
sudden changes in the availability of resources or unexpected user behavior.
Without proper mitigation these uncertainties can jeopardize the system goals.
Self-adaptation is a common approach to tackle such uncertainties. When the
system goals may be compromised, the self-adaptive system has to select the
best adaptation option to reconfigure by analyzing the possible adaptation
options, i.e., the adaptation space. Yet, analyzing large adaptation spaces
using rigorous methods can be resource- and time-consuming, or even be
infeasible. One approach to tackle this problem is by using online machine
learning to reduce adaptation spaces. However, existing approaches require
domain expertise to perform feature engineering to define the learner, and
support online adaptation space reduction only for specific goals. To tackle
these limitations, we present 'Deep Learning for Adaptation Space Reduction
Plus' -- DLASeR+ in short. DLASeR+ offers an extendable learning framework for
online adaptation space reduction that does not require feature engineering,
while supporting three common types of adaptation goals: threshold,
optimization, and set-point goals. We evaluate DLASeR+ on two instances of an
Internet-of-Things application with increasing sizes of adaptation spaces for
different combinations of adaptation goals. We compare DLASeR+ with a baseline
that applies exhaustive analysis and two state-of-the-art approaches for
adaptation space reduction that rely on learning. Results show that DLASeR+ is
effective with a negligible effect on the realization of the adaptation goals
compared to an exhaustive analysis approach, and supports three common types of
adaptation goals beyond the state-of-the-art approaches.
