VLDB 2026 Research / reviewers in the wild / expert
Xuechao Sun
dblp:234/6262
· DBLP profile ↗
6ranked-venue papers
0as first author
2since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 5 · 1 since 2021Theory of computation · 2 · 2 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Synthesizing ranking functions for loop programs via SVM
Xie Li, Yong Li 0031, Xuechao Sun, Andrea Turrini, Lijun Zhang 0001 |
Theor. Comput. Sci. | 4 |
| 2021 | Formal Verification of Consensus in the Taurus Distributed Database
Song Gao 0014, Bohua Zhan, Depeng Liu, Xuechao Sun, Yanan Zhi, David N. Jansen, Lijun Zhang 0001 |
FM | 4 |
| 2020 | Proving Non-inclusion of Büchi Automata Based on Monte Carlo Sampling
Yong Li 0031, Andrea Turrini, Xuechao Sun, Lijun Zhang 0001 |
ATVA | 3 |
| 2020 | SVMRanker: a general termination analysis framework of loop programs via SVMabstractDeciding termination of programs is probably the most famous problem in computer science. Synthesizing ranking functions for programs is a standard way to prove termination of programs. Currently, specific synthesis algorithms have to be developed for each specific type of programs. For instance, the synthesis of ranking functions for programs with linear variables updates is usually based on linear programming techniques and the like, while for programs with polynomial updates, it usually relies on semi-definite programming and the like. The same also applies to the synthesis of different types of ranking functions needed for proving program termination. Each time faced with a new type of programs and a new type of ranking functions, researchers have to spend a considerable amount of effort to develop specialized synthesis algorithms. In this paper, to save this extra effort, we present SVMRanker, a general framework for proving termination of programs, which is able to synthesize different types of ranking functions for programs with both linear and polynomial updates, based on Support-Vector Machines (SVM). We compare SVMRanker with the state-of-the-art tool LassoRanker on standard benchmarks. Empirical results show that SVMRanker is comparable with LassoRanker on programs with linear updates and can manage more programs with polynomial updates, making SVMRanker a valid complement to LassoRanker in proving program termination. Xie Li, Yong Li 0031, Xuechao Sun, Andrea Turrini, Lijun Zhang 0001 |
ESEC/SIGSOFT FSE | 4 |
| 2019 | Synthesizing Nested Ranking Functions for Loop Programs via SVM
Xuechao Sun, Yong Li 0031, Andrea Turrini, Lijun Zhang 0001 |
ICFEM | 2 |
| 2019 | ROLL 1.0: \omega -Regular Language Learning LibraryabstractWe present ROLL 1.0, an $$\omega $$ -regular language learning library with command line tools to learn and complement Büchi automata. This open source Java library implements all existing learning algorithms for the complete class of $$\omega $$ -regular languages. It also provides a learning-based Büchi automata complementation procedure that can be used as a baseline for automata complementation research. The tool supports both the Hanoi Omega Automata format and the BA format used by the tool RABIT. Moreover, it features an interactive Jupyter notebook environment that can be used for educational purpose. Yong Li 0031, Xuechao Sun, Andrea Turrini, Yu-Fang Chen 0001, Junnan Xu |
TACAS (1) | 2 |