VLDB 2026 Research / reviewers in the wild / expert
Maxwell Troy Bland
dblp:242/4523
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 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.
| Software engineering, system software, and programming languages
1 paper |
Program verification · 50% Program analysis · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program verification › type-based verification
refinement type verification |
0.4 | 1 | 2019 | Lazy counterfactual symbolic execution · PLDI 2019 |
Program analysis
symbolic execution |
0.4 | 1 | 2019 | Lazy counterfactual symbolic execution · PLDI 2019 |
Methods — techniques the papers use, named apart from their topics
symbolic execution · 0.4refinement types · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Lazy counterfactual symbolic executionabstractWe present counterfactual symbolic execution, a new approach that produces counterexamples that localize the causes of failure of static verification. First, we develop a notion of symbolic weak head normal form and use it to define lazy symbolic execution reduction rules for non-strict languages like Haskell. Second, we introduce counterfactual branching, a new method to identify places where verification fails due to imprecise specifications (as opposed to incorrect code). Third, we show how to use counterfactual symbolic execution to localize refinement type errors, by translating refinement types into assertions. We implement our approach in a new Haskell symbolic execution engine, G2, and evaluate it on a corpus of 7550 errors gathered from users of the LiquidHaskell refinement type system. We show that for 97.7% of these errors, G2 is able to quickly find counterexamples that show how the code or specifications must be fixed to enable verification. William T. Hallahan, Anton Xue, Maxwell Troy Bland, Ranjit Jhala, Ruzica Piskac |
PLDI | 3 |