EDBT 2026 Demo / reviewers in the wild / expert
Yongjiang Wang
dblp:282/5319
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0006-8477-9446ORCID · 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 |
Software testing · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing › mobile application testing
android app testing |
0.8 | 1 | 2024 | Reproducing Timing-Dependent GUI Flaky Tests in Android Apps via a Single Event Delay · ISSTA 2024 |
Software testing
regression testing |
0.8 | 1 | 2024 | Reproducing Timing-Dependent GUI Flaky Tests in Android Apps via a Single Event Delay · ISSTA 2024 |
Methods — techniques the papers use, named apart from their topics
selective event delay · 0.8event race inference · 0.8dynamic analysis · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Reproducing Timing-Dependent GUI Flaky Tests in Android Apps via a Single Event DelayabstractFlaky tests hinder the development process by exhibiting uncertain behavior in regression testing. A flaky test may pass in some runs and fail in others while running on the same code version. The non-deterministic outcome frequently misleads the developers into debugging non-existent faults in the code. To effectively debug the flaky tests, developers need to reproduce them. The industry de facto to reproduce flaky tests is to rerun them multiple times. However, rerunning a flaky test numerous times is time and resource-consuming. This work presents a technique for rapidly and reliably reproducing timing-dependent GUI flaky tests, acknowledged as the most common type of flaky tests in Android apps. Our insight is that flakiness in such tests often stems from event racing on GUI data. Given stack traces of a failure, our technique employs dynamic analysis to infer event races likely leading to the failure and reproduces it by selectively delaying only relevant events involved in these races. Thus, our technique can efficiently reproduce a failure within minimal test runs. The experiments conducted on 80 timing-dependent flaky tests collected from 22 widely-used Android apps show our technique is efficient in flaky test failure reproduction. Out of the 80 flaky tests, our technique could successfully reproduce 73 within 1.71 test runs on average. Notably, it exhibited extremely high reliability by consistently reproducing the failure for 20 runs. Xiaobao Cai, Yongjiang Wang, Abhishek Tiwari 0001, Xin Peng 0001 |
ISSTA | 3 |