EDBT 2026 Demo / reviewers in the wild / expert
Albert Xing
dblp:264/5385
· DBLP profile ↗
1ranked-venue papers
0as 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
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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Distributed systems · 33% Parallel and multicore computing · 33% Cloud and datacenter computing · 33% | |
| Software engineering, system software, and programming languages
1 paper |
Software maintenance and evolution · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed systems
distributed debugging |
0.4 | 1 | 2020 | Visualizing Distributed System Executions · ACM Trans. Softw. Eng. Methodol. 2020 |
Parallel and multicore computing › parallel programming environment
execution visualization |
0.4 | 1 | 2020 | Visualizing Distributed System Executions · ACM Trans. Softw. Eng. Methodol. 2020 |
Cloud and datacenter computing
log analysis |
0.4 | 1 | 2020 | Visualizing Distributed System Executions · ACM Trans. Softw. Eng. Methodol. 2020 |
Software maintenance and evolution
program comprehension |
0.1 | 1 | 2020 | Visualizing Distributed System Executions · ACM Trans. Softw. Eng. Methodol. 2020 |
Methods — techniques the papers use, named apart from their topics
user study · 0.9instrumentation · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Visualizing Distributed System ExecutionsabstractDistributed systems pose unique challenges for software developers. Understanding the system’s communication topology and reasoning about concurrent activities of system hosts can be difficult. The standard approach, analyzing system logs, can be a tedious and complex process that involves reconstructing a system log from multiple hosts’ logs, reconciling timestamps among hosts with non-synchronized clocks, and understanding what took place during the execution encoded by the log. This article presents a novel approach for tackling three tasks frequently performed during analysis of distributed system executions: (1) understanding the relative ordering of events, (2) searching for specific patterns of interaction between hosts, and (3) identifying structural similarities and differences between pairs of executions. Our approach consists of XVector , which instruments distributed systems to capture partial ordering information that encodes the happens-before relation between events, and ShiViz , which processes the resulting logs and presents distributed system executions as interactive time-space diagrams. Two user studies with a total of 109 students and a case study with 2 developers showed that our method was effective, helping participants answer statistically significantly more system-comprehension questions correctly, with a very large effect size. Ivan Beschastnikh, Perry Liu, Albert Xing, Patty Wang, Yuriy Brun, Michael D. Ernst |
ACM Trans. Softw. Eng. Methodol. | 3 |