Demo Data from EDBT 2026 / reviewers in the wild / expert
Innokentii Meleshchenko
dblp:396/5009
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0004-2539-7160ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 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 |
Concurrent programming · 61% Program verification · 30% Compilers and program optimization · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Concurrent programming
memory models |
0.9 | 1 | 2025 | Relaxed Memory Concurrency Re-executed · Proc. ACM Program. Lang. 2025 |
Program verification
model checking |
0.9 | 1 | 2025 | Relaxed Memory Concurrency Re-executed · Proc. ACM Program. Lang. 2025 |
Concurrent programming › memory models
weak memory models |
0.9 | 1 | 2025 | Relaxed Memory Concurrency Re-executed · Proc. ACM Program. Lang. 2025 |
Compilers and program optimization
compiler optimization |
0.3 | 1 | 2025 | Relaxed Memory Concurrency Re-executed · Proc. ACM Program. Lang. 2025 |
Methods — techniques the papers use, named apart from their topics
model checking · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Relaxed Memory Concurrency Re-executedabstractDefining a formal model for concurrency in programming languages that addresses conflicting requirements from programmers, compilers, and architectures has been a long-standing research question. It is widely believed that traditional axiomatic per-execution models that reason about individual executions do not suffice to address these conflicting requirements. Consequently, several multi-execution models were proposed that reason about multiple executions together. Although multi-execution models were major breakthroughs in satisfying several desired properties, these models are complicated, challenging to adapt to existing language specifications given in per-execution style, and they are typically not friendly to automated reasoning tools. In response, we propose a re-execution-based memory model (XMM). Debunking the beliefs around per-execution and multi-execution models, XMM is (almost) a per-execution model. XMM reasons about individual executions, but unlike traditional per-execution models, it relates executions by a re-execution principle. As such, the memory consistency axioms and the out-of-order re-execution mechanics are orthogonal in XMM, allowing to use it as a semantic framework parameterized by a given axiomatic memory model. We instantiated the XMM framework for the RC20 language model, and proved that the resulting model XC20 provides DRF guarantees and allows standard hardware mappings and compiler optimizations. Note-worthy, XC20 is the first model of its kind that also supports thread sequentialization optimization. Moreover, XC20 is also amenable to automated reasoning. To demonstrate this, we developed a sound model checker XMC and evaluated it on several concurrency benchmarks. Evgenii Moiseenko, Matteo Meluzzi, Innokentii Meleshchenko, Ivan Kabashnyi, Anton Podkopaev, Soham Chakraborty 0001 |
Proc. ACM Program. Lang. | 3 |