Demo Data from VLDB 2026 / reviewers in the wild / expert
David K. Baker
dblp:92/4563
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
User interface design and tools · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Debugging and program repair · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Debugging and program repair
visual debugging |
0.0 | 1 | 2004 | An explanation-based, visual debugger for one-way constraints · UIST 2004 |
Methods — techniques the papers use, named apart from their topics
explanation generation · 0.1dataflow graph visualization · 0.1constraint slicing · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | An explanation-based, visual debugger for one-way constraintsabstractThis paper describes a domain-specific debugger for one-way constraint solvers. The debugger makes use of several new techniques. First, the debugger displays only a portion of the dataflow graph, called a constraint slice, that is directly related to an incorrect variable. This technique helps the debugger scale to a system containing thousands of constraints. Second, the debugger presents a visual representation of the solver's data structures and uses color encodings to highlight changes to the data structures. Finally, the debugger allows the user to point to a variable that has an unexpected value and ask the debugger to suggest reasons for the unexpected value. The debugger makes use of information gathered during the constraint satisfaction process to generate plausible suggestions. Informal testing has shown that the explanatory capability and the color coding of the constraint solver's data structures are particularly useful in locating bugs in constraint code. Bradley T. Vander Zanden, David K. Baker |
UIST | 2 |