  Modern software systems are often highly configurable to tailor varied
requirements from diverse stakeholders. Understanding the mapping between
configurations and the desired performance attributes plays a fundamental role
in advancing the controllability and tuning of the underlying system, yet has
long been a dark hole of knowledge due to their black-box nature and the
enormous combinatorial configuration space. In this paper, using $86$M
evaluated configurations from three real-world systems on $32$ running
workloads, we conducted one of its kind fitness landscape analysis (FLA) for
configurable software systems. With comprehensive FLA methods, we for the first
time show that: $i)$ the software configuration landscapes are fairly rugged,
with numerous scattered local optima; $ii)$ nevertheless, the top local optima
are highly accessible, featuring significantly larger basins of attraction;
$iii)$ most inferior local optima are escapable with simple perturbations;
$iv)$ landscapes of the same system with different workloads share structural
similarities, which can be exploited to expedite heuristic search. Our results
also provide valuable insights on the design of tailored meta-heuristics for
configuration tuning; our FLA framework along with the collected data, build
solid foundation for future research in this direction.
