Demo Data from EDBT 2026 / reviewers in the wild / expert
Daniel Quinta
dblp:181/0873
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—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.
| Software engineering, system software, and programming languages
1 paper |
Debugging and program repair · 70% Concurrent programming · 30% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Concurrent programming
concurrency bugs |
0.2 | 1 | 2016 | Concurrency Debugging with Differential Schedule Projections · ACM Trans. Softw. Eng. Methodol. 2016 |
Debugging and program repair
concurrent program debugging |
0.2 | 1 | 2016 | Concurrency Debugging with Differential Schedule Projections · ACM Trans. Softw. Eng. Methodol. 2016 |
Debugging and program repair
fault localization |
0.2 | 1 | 2016 | Concurrency Debugging with Differential Schedule Projections · ACM Trans. Softw. Eng. Methodol. 2016 |
Debugging and program repair
root cause analysis |
0.1 | 1 | 2016 | Concurrency Debugging with Differential Schedule Projections · ACM Trans. Softw. Eng. Methodol. 2016 |
Methods — techniques the papers use, named apart from their topics
thread path profiling · 0.2symbolic constraint solving · 0.2differential schedule projections · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Concurrency Debugging with Differential Schedule ProjectionsabstractWe present Symbiosis: a concurrency debugging technique based on novel differential schedule projections (DSPs). A DSP shows the small set of memory operations and dataflows responsible for a failure, as well as a reordering of those elements that avoids the failure. To build a DSP, Symbiosis first generates a full, failing, multithreaded schedule via thread path profiling and symbolic constraint solving. Symbiosis selectively reorders events in the failing schedule to produce a nonfailing, alternate schedule . A DSP reports the ordering and dataflow differences between the failing and nonfailing schedules. Our evaluation on buggy real-world software and benchmarks shows that, in practical time, Symbiosis generates DSPs that both isolate the small fraction of event orders and dataflows responsible for the failure and report which event reorderings prevent failing. In our experiments, DSPs contain 90% fewer events and 96% fewer dataflows than the full failure-inducing schedules. We also conducted a user study that shows that, by allowing developers to focus on only a few events, DSPs reduce the amount of time required to understand the bug’s root cause and find a valid fix. Nuno Machado, Daniel Quinta, Brandon Lucia, Luís E. T. Rodrigues |
ACM Trans. Softw. Eng. Methodol. | 2 |