Demo Data from VLDB 2026 / reviewers in the wild / expert
Levin N. Winter
dblp:346/8791
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-1983-8626ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 first-author · 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 |
Software testing · 100% | |
| Network and information security
1 paper |
Blockchain and cryptocurrency security · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Distributed systems · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing › mutation testing
fault injection |
0.7 | 1 | 2023 | Randomized Testing of Byzantine Fault Tolerant Algorithms · Proc. ACM Program. Lang. 2023 |
Software testing
random testing |
0.7 | 1 | 2023 | Randomized Testing of Byzantine Fault Tolerant Algorithms · Proc. ACM Program. Lang. 2023 |
Distributed systems › fault tolerance
byzantine fault tolerance |
0.7 | 1 | 2023 | Randomized Testing of Byzantine Fault Tolerant Algorithms · Proc. ACM Program. Lang. 2023 |
Methods — techniques the papers use, named apart from their topics
small-scope mutation · 2.0randomized testing · 2.0fuzzing · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Randomized Testing of Byzantine Fault Tolerant AlgorithmsabstractByzantine fault-tolerant algorithms promise agreement on a correct value, even if a subset of processes can deviate from the algorithm arbitrarily. While these algorithms provide strong guarantees in theory, in practice, protocol bugs and implementation mistakes may still cause them to go wrong. This paper introduces ByzzFuzz, a simple yet effective method for automatically finding errors in implementations of Byzantine fault-tolerant algorithms through randomized testing. ByzzFuzz detects fault-tolerance bugs by injecting randomly generated network and process faults into their executions. To navigate the space of possible process faults, ByzzFuzz introduces small-scope message mutations which mutate the contents of the protocol messages by applying small changes to the original message either in value (e.g., by incrementing the round number) or in time (e.g., by repeating a proposal value from a previous message). We find that small-scope mutations, combined with insights from the testing and fuzzing literature, are effective at uncovering protocol logic and implementation bugs in real-world fault-tolerant systems. We implemented ByzzFuzz and applied it to test the production implementations of two popular blockchain systems, Tendermint and Ripple, and an implementation of the seminal PBFT protocol. ByzzFuzz detected several bugs in the implementation of PBFT, a potential liveness violation in Tendermint, and materialized two theoretically described vulnerabilities in Ripple’s XRP Ledger Consensus Algorithm. Moreover, we discovered a previously unknown fault-tolerance bug in the production implementation of Ripple, which is confirmed by the developers and fixed. Levin N. Winter, Florena Buse, Daan de Graaf, Klaus von Gleissenthall, Burcu Kulahcioglu Ozkan |
Proc. ACM Program. Lang. | 1 |