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Harun Yilmaz

dblp:437/3899 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0006-8132-2002ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021Theory of computation · 1 · 1 since 2021

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.

Theoretical computer science
1 paper
Computational complexity · 39% Automata and formal languages · 30% Automated reasoning and model checking · 30%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Automated reasoning and model checking
quantitative verification
1.012026
Extending QuAK with Nested Quantitative Automata · CAV (2) 2026
Computational complexity › boolean function complexity
threshold problem
1.012026
Extending QuAK with Nested Quantitative Automata · CAV (2) 2026
Computational complexity › decidability › decision problems for automata
emptiness problem
0.312026
Extending QuAK with Nested Quantitative Automata · CAV (2) 2026

Methods — techniques the papers use, named apart from their topics

flattening procedures · 1.0
YearPublicationVenuePosition
2026 Extending QuAK with Nested Quantitative Automata
abstract
Abstract Quantitative automata (QAs) extend finite-state automata on infinite words with weighted transitions to specify quantitative system properties. However, their finite weight sets rule out properties like average response time, where response times can be arbitrarily large. Nested quantitative automata (NQAs) overcome this limitation: a parent automaton spawns child automata to compute unbounded values over finite infixes and aggregates them into a final result. Despite this expressiveness, NQAs have lacked practical tool support to date. We close this gap by extending the Quantitative Automata Kit (QuAK), a software tool for QA analysis, to support NQAs. Our core contribution is implementing a suite of flattening procedures that reduce NQAs to QAs, leveraging QuAK’s existing decision procedures. These reductions preserve the answers to threshold decision problems, while allowing users to specify properties in the more expressive NQA formalism. The tool handles all combinations of parent aggregators (including limits and averages) and child functions (extrema and monotonic or bounded summations) for which emptiness and universality are known to be decidable. Experiments on response-time and resource-consumption benchmarks demonstrate QuAK’s effectiveness.
Thomas A. Henzinger, Nicolas Mazzocchi, N. Ege Saraç, Harun Yilmaz
CAV (2)4