Learning and predicting the performance of given software configurations are of high
importance to many software engineering activities. While configurable software systems will …

Context: Many software systems are highly configurable. Different configuration options
could lead to varying performances of the system. It is difficult to measure system …

Many research studies predict the performance of configurable software using machine
learning techniques, thus requiring large amounts of data. Transfer learning aims to reduce …

Software engineers often have to estimate the performance of a software system before
having full knowledge of the system parameters, such as workload and operational profile …

To assure performance on the fly, planning is arguably one of the most important steps for
self-adaptive systems (SASs), especially when they are highly configurable with a daunting …

Data science applications have become widespread as a means to extract knowledge from
large datasets. Such applications are often characterized by highly heterogeneous and …

Our software framework, Proteus, treats adaptation as a first-class object, enabling rapid
development of robust, adaptive applications. Proteus developers specify their programs' …

Users of highly-configurable software systems often want to optimize a particular objective
such as improving a functional outcome or increasing system performance. One approach is …

Identifying scalability bottlenecks in parallel applications is a vital but also laborious and
expensive task. Empirical performance models have proven to be helpful to find such …

One of the key challenges in designing machine learning systems is to determine the right
balance amongst several objectives, which also oftentimes are incommensurable and …