Umut Yigit Dural

dblp:437/7227 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0002-8071-3600ORCID · 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.

Software engineering, system software, and programming languages
1 paper
Program verification · 100%
Theoretical computer science
1 paper
Automated reasoning and model checking · 77% Logic in computer science · 23%

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

TopicWeightPapersLastEvidence papers
Program verification
deductive verification
1.012026
Caesar: A Deductive Verifier for Probabilistic Programs · CAV (3) 2026
Program verification › probabilistic verification
probabilistic program verification
1.012026
Caesar: A Deductive Verifier for Probabilistic Programs · CAV (3) 2026
Automated reasoning and model checking › model checking
probabilistic model checking
1.012026
Caesar: A Deductive Verifier for Probabilistic Programs · CAV (3) 2026
Logic in computer science
program logic
0.312026
Caesar: A Deductive Verifier for Probabilistic Programs · CAV (3) 2026

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

verification condition generation · 2.0probabilistic model checking · 2.0SMT solving · 2.0
YearPublicationVenuePosition
2026 Caesar: A Deductive Verifier for Probabilistic Programs
abstract
Abstract is a deductive verifier for probabilistic programs. At its core lies , a quantitative intermediate verification language based on the real-valued logic . allows users to express a probabilistic program, its specifications, and proof rules in a programming-language style, so that new proof rules can be easily integrated into the verifier. translates programs into verification conditions, which are then checked using the Z3 SMT solver. It also includes a backend based on probabilistic model checking for a subset of . We report on the results of five years of development of , highlighting its main features and architecture. In particular, we describe recent improvements such as additional proof rules, a model-checking backend, and better diagnostics.
Philipp Schröer, Kevin Batz, Umut Yigit Dural, Darion Haase, Benjamin Lucien Kaminski, Joost-Pieter Katoen, Christoph Matheja
CAV (3)3