Demo Data from SIGIR 2026 / reviewers in the wild / expert
Martin A. T. Handley
dblp:231/5133
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 first-author
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 |
Programming languages and type systems · 39% Program analysis · 30% Program verification · 30% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis
cost analysis |
0.4 | 1 | 2020 | Liquidate your assets: reasoning about resource usage in liquid Haskell · Proc. ACM Program. Lang. 2020 |
Programming languages and type systems › type systems
refinement types |
0.4 | 1 | 2020 | Liquidate your assets: reasoning about resource usage in liquid Haskell · Proc. ACM Program. Lang. 2020 |
Program verification › quantitative verification
resource verification |
0.4 | 1 | 2020 | Liquidate your assets: reasoning about resource usage in liquid Haskell · Proc. ACM Program. Lang. 2020 |
Methods — techniques the papers use, named apart from their topics
refinement type checking · 0.4cost invariant inference · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Liquidate your assets: reasoning about resource usage in liquid HaskellabstractLiquid Haskell is an extension to the type system of Haskell that supports formal reasoning about program correctness by encoding logical properties as refinement types. In this article, we show how Liquid Haskell can also be used to reason about program efficiency in the same setting. We use the system's existing verification machinery to ensure that the results of our cost analysis are valid, together with custom invariants for particular program contexts to ensure that the results of our analysis are precise. To illustrate our approach, we analyse the efficiency of a wide range of popular data structures and algorithms, and in doing so, explore various notions of resource usage. Our experience is that reasoning about efficiency in Liquid Haskell is often just as simple as reasoning about correctness, and that the two can naturally be combined. Martin A. T. Handley, Niki Vazou, Graham Hutton |
Proc. ACM Program. Lang. | 1 |