Demo Data from EDBT 2026 / reviewers in the wild / expert
Jiazhan Xie
dblp:299/8754
· DBLP profile ↗
2ranked-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 · 2 · 2 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
2 papers |
Empirical software engineering · 41% Software maintenance and evolution · 36% Program analysis · 23% | |
| Databases, data mining, and information retrieval
1 paper |
Knowledge graphs · 100% |
Topics — the 5 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering
mining software repositories |
0.6 | 1 | 2022 | How to formulate specific how-to questions in software development? · ESEC/SIGSOFT FSE 2022 |
Empirical software engineering › mining software repositories › question and answer sites
stack overflow analysis |
0.6 | 1 | 2022 | How to formulate specific how-to questions in software development? · ESEC/SIGSOFT FSE 2022 |
Software maintenance and evolution › software documentation
API documentation analysis |
0.5 | 1 | 2021 | Learning-based extraction of first-order logic representations of API directives · ESEC/SIGSOFT FSE 2021 |
Software maintenance and evolution › software documentation
documentation analysis |
0.5 | 1 | 2021 | Learning-based extraction of first-order logic representations of API directives · ESEC/SIGSOFT FSE 2021 |
Program analysis
static analysis |
0.1 | 1 | 2021 | Learning-based extraction of first-order logic representations of API directives · ESEC/SIGSOFT FSE 2021 |
Methods — techniques the papers use, named apart from their topics
knowledge graph mining · 1.1empirical study · 1.1natural language processing · 0.5machine learning · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | How to formulate specific how-to questions in software development?abstractDevelopers often ask how-to questions using search engines, technical Q&A communities, and interactive Q&A systems to seek help for specific programming tasks. However, they often do not formulate the questions in a specific way, making it hard for the systems to return the best answers. We propose an approach (TaskKG4Q) that interactively helps developers formulate a programming related how-to question. TaskKG4Q is using a programming task knowledge graph (task KG in short) mined from Stack Overflow questions, which provides a hierarchical conceptual structure for tasks in terms of [actions], [objects], and [constraints]. An empirical evaluation of the intrinsic quality of the task KG revealed that 75.0% of the annotated questions in the task KG are correct. The comparison between TaskKG4Q and two baselines revealed that TaskKG4Q can help developers formulate more specific how-to questions. More so, an empirical study with novice programmers revealed that they write more effective questions for finding answers to their programming tasks on Stack Overflow. Mingwei Liu 0002, Xin Peng 0001, Andrian Marcus, Christoph Treude, Jiazhan Xie, Huanjun Xu |
ESEC/SIGSOFT FSE | 5 |
| 2021 | Learning-based extraction of first-order logic representations of API directivesabstractDevelopers often rely on API documentation to learn API directives, i.e., constraints and guidelines related to API usage. Failing to follow API directives may cause defects or improper implementations. Since there are no industry-wide standards on how to document API directives, they take many forms and are often hard to understand by developers or challenging to parse with tools. Mingwei Liu 0002, Xin Peng 0001, Andrian Marcus, Christoph Treude, Xuefang Bai, Gang Lyu, Jiazhan Xie, Xiaoxin Zhang |
ESEC/SIGSOFT FSE | 7 |