Deep Configuration Performance Learning: A Systematic Survey and Taxonomy

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.