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Martin A. T. Handley

dblp:231/5133 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Program analysis
cost analysis
0.412020
Liquidate your assets: reasoning about resource usage in liquid Haskell · Proc. ACM Program. Lang. 2020
Programming languages and type systems › type systems
refinement types
0.412020
Liquidate your assets: reasoning about resource usage in liquid Haskell · Proc. ACM Program. Lang. 2020
Program verification › quantitative verification
resource verification
0.412020
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
YearPublicationVenuePosition
2020 Liquidate your assets: reasoning about resource usage in liquid Haskell
abstract
Liquid 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