Demo Data from VLDB 2026 / reviewers in the wild / expert
Joy Agustin
dblp:30/5749
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none
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 |
Empirical software engineering · 87% Software maintenance and evolution · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering
developer studies |
0.0 | 1 | 2003 | Beyond the Personal Software Process: Metrics collection and analysis for the differently disciplined · ICSE 2003 |
Empirical software engineering
mining software repositories |
0.0 | 1 | 2003 | Beyond the Personal Software Process: Metrics collection and analysis for the differently disciplined · ICSE 2003 |
Software maintenance and evolution
software process improvement |
0.0 | 1 | 2003 | Beyond the Personal Software Process: Metrics collection and analysis for the differently disciplined · ICSE 2003 |
Methods — techniques the papers use, named apart from their topics
automated metrics collection · 0.0automated data analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Beyond the Personal Software Process: Metrics collection and analysis for the differently disciplinedabstractPedagogues such as the Personal Software Process (PSP) shift metrics definition, collection, and analysis from the organizational level to the individual level. While case study research indicates that the PSP can provide software engineering students with empirical support for improving estimation and quality assurance, there is little evidence that many students continue to use the PSP when no longer required to do so. Our research suggests that this "PSP adoption problem" may be due to two problems: the high overhead of PSP-style metrics collection and analysis, and the requirement that PSP users "context switch" between product development and process recording. This paper overviews our initial PSP experiences, our first attempt to solve the PSP adoption problem with the LEAP system, and our current approach called Hackystat. This approach fully automates both data collection and analysis, which eliminates overhead and context switching. However, Hackystat changes the kind of metrics data that is collected, and introduces new privacy-related adoption issues of its own. Philip M. Johnson, Hongbing Kou, Joy Agustin, Carleton A. Moore, Jitender Miglani, Shenyan Zhen, William E. J. Doane |
ICSE | 3 |