Live programming environments aim to provide programmers (and sometimes audiences)
with continuous feedback about a program's dynamic behavior as it is being edited. The …

In this paper, we present a method for explaining the results produced by dynamic
programming (DP) algorithms. Our approach is based on retaining a granular representation …

Provenance, or information about the origin or derivation of data, is important for assessing
the trustworthiness of data and identifying and correcting mistakes. Most prior …

Program traces are a widely used representation for explaining the dynamic behavior of
programs. They help to make sense of computations and also support the location and …

Program traces are a sound basis for explaining the dynamic behavior of programs. Alas,
program traces can grow big very quickly, even for small programs, which diminishes their …

We introduce a representation for generating explanations for the outcomes of combinatorial
optimization algorithms. The two key ideas are (A) to maintain fine-grained representations …

We present new language-based dynamic analysis techniques for linking visualisations and
other structured outputs to data in a fine-grained way, allowing users to explore how data …

Language-integrated provenance builds on language-integrated query techniques to make
provenance information explaining query results readily available to programmers. In …

MADMAX is a Haskell-embedded DSL for multi-attribute, multi-layered decision making. An
important feature of this DSL is the ability to generate explanations of why a computed …

Live programming provides feedback on run-time behavior by visualizing concrete values of
expressions close to the source code. When using such a local perspective on run-time …