Demo Data from EDBT 2026 / reviewers in the wild / expert
Yuji Kamayachi
dblp:71/5785
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 1989
—ORCID · none
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.
| Software engineering, system software, and programming languages
2 papers |
Empirical software engineering · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering › software defect prediction
program error prediction |
0.0 | 2 | 1989 | An Empirical Study of a Model for Program Error Prediction · IEEE Trans. Software Eng. 1989 An Empirical Study of a Model for Program Error Prediction · ICSE 1985 |
Empirical software engineering › mining software repositories
defect prediction |
0.0 | 1 | 1989 | An Empirical Study of a Model for Program Error Prediction · IEEE Trans. Software Eng. 1989 |
Empirical software engineering
software analytics |
0.0 | 1 | 1985 | An Empirical Study of a Model for Program Error Prediction · ICSE 1985 |
Empirical software engineering
fault prediction |
0.0 | 1 | 1985 | An Empirical Study of a Model for Program Error Prediction · ICSE 1985 |
Methods — techniques the papers use, named apart from their topics
statistical analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1989 | An Empirical Study of a Model for Program Error PredictionabstractA model is presented for estimating the number of errors remaining in a program at the beginning of the testing phase of development. The relationships between the errors occurring in a program and the various factors that affect software development, such as programmer skill, are statistically analyzed. The model is then derived using the factors significantly identified in the analysis. On the basis of data collected during the development of large-scale software systems, it is shown that factors such as frequency of program specification change, programmer skill, and volume of program design documentation are significant and that the model based on these factors is more reliable than conventional error prediction methods based on program size alone.> Muneo Takahashi, Yuji Kamayachi |
IEEE Trans. Software Eng. | 2 |
| 1985 | An Empirical Study of a Model for Program Error Prediction
Muneo Takahashi, Yuji Kamayachi |
ICSE | 2 |