Demo Data from EDBT 2026 / reviewers in the wild / expert
Olga Lucero Vega-Márquez
dblp:242/3904
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0003-2164-4801ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1
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 maintenance and evolution · 50% Empirical software engineering · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering
mining software repositories |
0.4 | 1 | 2019 | Software documentation issues unveiled · ICSE 2019 |
Software maintenance and evolution
software documentation |
0.4 | 1 | 2019 | Software documentation issues unveiled · ICSE 2019 |
Methods — techniques the papers use, named apart from their topics
empirical study · 0.4artifact categorization · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Software documentation issues unveiledabstract(Good) Software documentation provides developers and users with a description of what a software system does, how it operates, and how it should be used. For example, technical documentation (e.g., an API reference guide) aids developers during evolution/maintenance activities, while a user manual explains how users are to interact with a system. Despite its intrinsic value, the creation and the maintenance of documentation is often neglected, negatively impacting its quality and usefulness, ultimately leading to a generally unfavourable take on documentation. Previous studies investigating documentation issues have been based on surveying developers, which naturally leads to a somewhat biased view of problems affecting documentation. We present a large scale empirical study, where we mined, analyzed, and categorized 878 documentation-related artifacts stemming from four different sources, namely mailing lists, Stack Overflow discussions, issue repositories, and pull requests. The result is a detailed taxonomy of documentation issues from which we infer a series of actionable proposals both for researchers and practitioners. Emad Aghajani, Csaba Nagy 0001, Olga Lucero Vega-Márquez, Mario Linares-Vásquez, Laura Moreno, Gabriele Bavota, Michele Lanza 0001 |
ICSE | 3 |