Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transfer Learning for Improving Model Predictions in Highly Configurable
  Software   Modern software systems are built to be used in dynamic environments using
configuration capabilities to adapt to changes and external uncertainties. In a
self-adaptation context, we are often interested in reasoning about the
performance of the systems under different configurations. Usually, we learn a
black-box model based on real measurements to predict the performance of the
system given a specific configuration. However, as modern systems become more
complex, there are many configuration parameters that may interact and we end
up learning an exponentially large configuration space. Naturally, this does
not scale when relying on real measurements in the actual changing environment.
We propose a different solution: Instead of taking the measurements from the
real system, we learn the model using samples from other sources, such as
simulators that approximate performance of the real system at low cost. We
define a cost model that transform the traditional view of model learning into
a multi-objective problem that not only takes into account model accuracy but
also measurements effort as well. We evaluate our cost-aware transfer learning
solution using real-world configurable software including (i) a robotic system,
(ii) 3 different stream processing applications, and (iii) a NoSQL database
system. The experimental results demonstrate that our approach can achieve (a)
a high prediction accuracy, as well as (b) a high model reliability.

15
The categories of this paper are: nucl-ex
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The categories of this paper are: stat
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
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The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
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The categories of this paper are: funct-an
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: eess