Deep Configuration Performance Learning: A Systematic Survey and
  Taxonomy
Jingzhi Gong Tao Chen
Performance is arguably the most crucial attribute that reflects the behavior
of a configurable software system. However, given the increasing scale and
complexity of modern software, modeling and predicting how various
configurations can impact performance becomes one of the major challenges in
software maintenance. As such, performance is often modeled without having a
thorough knowledge of the software system, but relying mainly on data, which
fits precisely with the purpose of deep learning.
  In this paper, we conduct a comprehensive review exclusively on the topic of
deep learning for performance learning of configurable software, covering 948
searched papers spanning six indexing services, based on which 85 primary
papers were extracted and analyzed. Our results summarize the key topics and
statistics on how the configuration data is prepared; how the deep
configuration performance learning model is built; how the model is evaluated
and how they are exploited in different tasks related to software
configuration. We also identify the good practice and the potentially
problematic phenomena from the studies surveyed, together with insights on
future opportunities for the field. To promote open science, all the raw
results of this survey can be accessed at our repository:
https://github.com/ideas-labo/DCPL-SLR.