Demo Data from EDBT 2026 / reviewers in the wild / expert
Xiangxing Qian
dblp:207/7171
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · unresolved
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 |
Software testing · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing
test input generation |
0.3 | 1 | 2017 | EHBDroid: beyond GUI testing for Android applications · ASE 2017 |
Software testing › mobile application testing
android app testing |
0.1 | 1 | 2017 | EHBDroid: beyond GUI testing for Android applications · ASE 2017 |
Software testing
GUI testing |
0.1 | 1 | 2017 | EHBDroid: beyond GUI testing for Android applications · ASE 2017 |
Methods — techniques the papers use, named apart from their topics
coverage measurement · 0.3callback invocation · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | EHBDroid: beyond GUI testing for Android applicationsabstractWith the prevalence of Android-based mobile devices, automated testing for Android apps has received increasing attention. However, owing to the large variety of events that Android supports, test input generation is a challenging task. In this paper, we present a novel approach and an open source tool called EHBDroid for testing Android apps. In contrast to conventional GUI testing approaches, a key novelty of EHBDroid is that it does not generate events from the GUI, but directly invokes callbacks of event handlers. By doing so, EHBDroid can efficiently simulate a large number of events that are difficult to generate by traditional UI-based approaches. We have evaluated EHBDroid on a collection of 35 real-world large-scale Android apps and compared its performance with two state-of-the-art UI-based approaches, Monkey and Dynodroid. Our experimental results show that EHBDroid is significantly more effective and efficient than Monkey and Dynodroid: in a much shorter time, EHBDroid achieves as much as 22.3% higher statement coverage (11.1% on average) than the other two approaches, and found 12 bugs in these benchmarks, including 5 new bugs that the other two failed to find. Wei Song 0003, Xiangxing Qian, Jeff Huang 0001 |
ASE | 2 |