Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jialiang Chang
dblp:184/8064
· DBLP profile ↗
4ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 4 · 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 · 67% Program analysis · 33% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Cloud and datacenter computing · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis › symbolic execution
dynamic symbolic execution |
0.2 | 1 | 2016 | GUICat: GUI testing as a service · ASE 2016 |
Software testing › test generation › test sequence generation
event sequence generation |
0.2 | 1 | 2016 | GUICat: GUI testing as a service · ASE 2016 |
Software testing
GUI testing |
0.2 | 1 | 2016 | GUICat: GUI testing as a service · ASE 2016 |
Cloud and datacenter computing
cloud-based testing |
0.1 | 1 | 2016 | GUICat: GUI testing as a service · ASE 2016 |
Methods — techniques the papers use, named apart from their topics
parallel algorithm · 0.5concolic execution · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | sVerify: Verifying Smart Contracts Through Lazy Annotation and Learning
Ling Shi 0002, Jiaying Li 0001, Jialiang Chang, Jun Sun 0001, Zijiang Yang 0006 |
ISoLA | 4 |
| 2019 | sCompile: Critical Path Identification and Analysis for Smart Contracts
Jialiang Chang, Jun Sun 0001, Yan Cai 0001, Zijiang Yang 0006 |
ICFEM | 1 |
| 2017 | Automated Testing of Definition-Use Data Flow for Multithreaded ProgramsabstractWith the advent of multicore processors, there is a trend towards multithreading to take advantage of parallel computing resources. Due to greatly increased complexity, programmers need effective testing methodology that can thoroughly test multithreaded programs. There has been significant progress based on symbolic execution that attempts to exhaustively explore all the intra-thread paths and inter-thread interleavings. However, such testing approach faces two insuperable challenges. Firstly, exploring an astronomically large number of paths and interleavings limits its scalability. Secondly, a path itself does not directly help programmers understand program behavior. In this paper, we propose an alternate testing methodology that focuses on definition-use data flow instead of paths/interleavings. Such approach not only leads to orders of magnitude reduction in testing complexity, but also gives programmers direct help on examining the shared variable usage in a multithreaded program. Xiaodong Zhang 0014, Zijiang Yang 0006, Jialiang Chang, Yu Hao 0006, Ting Liu 0002 |
ICST | 5 |
| 2016 | GUICat: GUI testing as a serviceabstractGUIs are event-driven applications where the flow of the program is determined by user actions such as mouse clicks and key presses. GUI testing is a challenging task not only because of the combinatorial explosion in the number of event sequences, but also because of the difficulty to cover the large number of data values. We propose GUICat, the first cloud based GUI testing framework that simultaneously generates event sequences and data values. It is a white-box GUI testing tool that augments traditional sequence generation techniques with concolic execution. We also propose a cloudbased parallel algorithm for mitigating both event sequence explosion and data value explosion, by distributing the concolic execution tasks over public clouds such as Amazon EC2. We have evaluated the tool on standard GUI testing benchmarks and showed that GUICat significantly outperforms state-of-the-art GUI testing tools. The video demo URL is https://youtu.be/rfnnQOmZqj4. Jialiang Chang, Zijiang Yang 0006, Chao Wang 0001 |
ASE | 2 |