Demo Data from SIGIR 2026 / reviewers in the wild / expert
Wenchuang Liu
dblp:207/7182
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0002-5256-6917ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2
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.
| Databases, data mining, and information retrieval
1 paper |
Data models and query languages · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Empirical software engineering · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data models and query languages › natural language interface › natural language interface to database
text-to-SQL |
0.3 | 1 | 2017 | TiQi: a natural language interface for querying software project data · ASE 2017 |
Empirical software engineering
software analytics |
0.3 | 1 | 2017 | TiQi: a natural language interface for querying software project data · ASE 2017 |
Methods — techniques the papers use, named apart from their topics
natural language processing · 0.6SQL generation · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Assisting engineers extracting requirements on components from domain documents
Xiaoli Lian, Wenchuang Liu, Li Zhang 0029 |
Inf. Softw. Technol. | 2 |
| 2017 | TiQi: a natural language interface for querying software project dataabstractSoftware projects produce large quantities of data such as feature requests, requirements, design artifacts, source code, tests, safety cases, release plans, and bug reports. If leveraged effectively, this data can be used to provide project intelligence that supports diverse software engineering activities such as release planning, impact analysis, and software analytics. However, project stakeholders often lack skills to formulate complex queries needed to retrieve, manipulate, and display the data in meaningful ways. To address these challenges we introduce TiQi, a natural language interface, which allows users to express software-related queries verbally or written in natural language. TiQi is a web-based tool. It visualizes available project data as a prompt to the user, accepts Natural Language (NL) queries, transforms those queries into SQL, and then executes the queries against a centralized or distributed database. Raw data is stored either directly in the database or retrieved dynamically at runtime from case tools and repositories such as Github and Jira. The transformed query is visualized back to the user as SQL and augmented UML, and raw data results are returned. Our tool demo can be found on YouTube at the following link:http://tinyurl.com/TIQIDemo. Jinfeng Lin, Yalin Liu, Jin L. C. Guo, Jane Cleland-Huang, William Goss, Wenchuang Liu, Sugandha Lohar, Natawut Monaikul, Alexander Rasin |
ASE | 6 |