Applying machine learning in self-adaptive systems: A systematic literature review
Recently, we have been witnessing a rapid increase in the use of machine learning
techniques in self-adaptive systems. Machine learning has been used for a variety of …
techniques in self-adaptive systems. Machine learning has been used for a variety of …
Learning software configuration spaces: A systematic literature review
Most modern software systems (operating systems like Linux or Android, Web browsers like
Firefox or Chrome, video encoders like ffmpeg, x264 or VLC, mobile and cloud applications …
Firefox or Chrome, video encoders like ffmpeg, x264 or VLC, mobile and cloud applications …
High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery
K McCullough, T Williams, K Mingle… - Physical Chemistry …, 2020 - pubs.rsc.org
High throughput experimentation in heterogeneous catalysis provides an efficient solution to
the generation of large datasets under reproducible conditions. Knowledge extraction from …
the generation of large datasets under reproducible conditions. Knowledge extraction from …
Transfer learning for performance modeling of configurable systems: An exploratory analysis
Modern software systems provide many configuration options which significantly influence
their non-functional properties. To understand and predict the effect of configuration options …
their non-functional properties. To understand and predict the effect of configuration options …
White-box analysis over machine learning: Modeling performance of configurable systems
Performance-influence models can help stakeholders understand how and where
configuration options and their interactions influence the performance of a system. With this …
configuration options and their interactions influence the performance of a system. With this …
Bliss: auto-tuning complex applications using a pool of diverse lightweight learning models
As parallel applications become more complex, auto-tuning becomes more desirable,
challenging, and time-consuming. We propose, Bliss, a novel solution for auto-tuning …
challenging, and time-consuming. We propose, Bliss, a novel solution for auto-tuning …
Learning to sample: Exploiting similarities across environments to learn performance models for configurable systems
Most software systems provide options that allow users to tailor the system in terms of
functionality and qualities. The increased flexibility raises challenges for understanding the …
functionality and qualities. The increased flexibility raises challenges for understanding the …
Unicorn: Reasoning about configurable system performance through the lens of causality
Modern computer systems are highly configurable, with the total variability space sometimes
larger than the number of atoms in the universe. Understanding and reasoning about the …
larger than the number of atoms in the universe. Understanding and reasoning about the …
Sampling effect on performance prediction of configurable systems: A case study
Numerous software systems are highly configurable and provide a myriad of configuration
options that users can tune to fit their functional and performance requirements (eg …
options that users can tune to fit their functional and performance requirements (eg …
Machine learning meets quantitative planning: Enabling self-adaptation in autonomous robots
Modern cyber-physical systems (eg, robotics systems) are typically composed of physical
and software components, the characteristics of which are likely to change over time …
and software components, the characteristics of which are likely to change over time …