Accurately predicting program behaviors (e.g., locality, dependency,
method calling frequency) is fundamental for program optimizations and
runtime adaptations. Despite decades of remarkable progress, prior
studies have not systematically exploited program inputs, a deciding
factor for program behaviors.

Triggered by the strong and predictive correlations between program
inputs and behaviors that recent studies have uncovered, this work
proposes to include program inputs into the focus of program behavior
analysis, cultivating a new paradigm named input-centric program
behavior analysis. This new approach consists of three components,
forming a three-layer pyramid. At the base is program input
characterization, a component for resolving the complexity in program
raw inputs and the extraction of important features. In the middle is
input-behavior modeling, a component for recognizing and modeling the
correlations between charcterized input features and program
behaviors. These two components constitute input-centric program
behavior analysis, which (ideally) is able to predict the large-scope
behaviors of a program's execution as soon as the execution starts.
The top layer of the pyramid is input-centric adaptation, which
capitalizes on the novel opportunities that the first two components
create to facilitate proactive adaptation for program optimizations.

By centering on program inputs, the new approach resolves a
proactivity-adaptivity dilemma inherent in previous techniques. Its
benefits are demonstrated through proactive dynamic optimizations and
version selection, yielding significant performance improvement on a
set of Java and C programs.