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Jiazhan Xie

dblp:299/8754 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Empirical software engineering
mining software repositories
0.612022
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
0.612022
How to formulate specific how-to questions in software development? · ESEC/SIGSOFT FSE 2022
Software maintenance and evolution › software documentation
API documentation analysis
0.512021
Learning-based extraction of first-order logic representations of API directives · ESEC/SIGSOFT FSE 2021
Software maintenance and evolution › software documentation
documentation analysis
0.512021
Learning-based extraction of first-order logic representations of API directives · ESEC/SIGSOFT FSE 2021
Program analysis
static analysis
0.112021
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
YearPublicationVenuePosition
2022 How to formulate specific how-to questions in software development?
abstract
Developers 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 FSE5
2021 Learning-based extraction of first-order logic representations of API directives
abstract
Developers 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 FSE7