Many applications need to respond to incremental modifications to data. Being incremental,
such modification often require incremental modifications to the output, making it possible to …

Many researchers have proposed programming languages that support incremental
computation (IC), which allows programs to be efficiently re-executed after a small change to …

We present D4, a fast concurrency analysis framework that detects concurrency bugs (eg,
data races and deadlocks) interactively in the programming phase. As developers add …

Self-adjusting computation enables writing programs that can automatically and efficiently
respond to changes to their data (eg, inputs). The idea behind the approach is to store all …

Self-adjusting computation offers a language-centric approach to writing programs that can
automatically respond to modifications to their data (eg, inputs). Except for several domain …

Recent work on adaptive functional programming (AFP) developed techniques for writing
programs that can respond to modifications to their data by performing change propagation …

We present DITTO, an automatic incrementalizer for dynamic, side-effect-free data structure
invariant checks. Incrementalization speeds up the execution of a check by reusing its …

Dataflow languages provide natural support for specifying constraints between objects in
dynamic applications, where programs need to react efficiently to changes of their …

Motivated by stochastic systems in which observed evidence and conditional dependencies
between states of the network change over time, and certain quantities of interest (marginal …

Self-adjusting computation is an evaluation model in which programs can respond efficiently
to small changes to their input data by using a change-propagation mechanism that updates …