Demo Data from EDBT 2026 / reviewers in the wild / expert
James M. Topolski
dblp:297/3082
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1982
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 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 · 77% Empirical software engineering · 23% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution
code readability |
0.0 | 1 | 1982 | Analyzer-generated and human-judged predictors of computer program readability · CHI 1982 |
Methods — techniques the papers use, named apart from their topics
software science measures · 0.0regression analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1982 | Analyzer-generated and human-judged predictors of computer program readabilityabstractThe readability of a computer program has recently attained a high level of interest deriving in part from its expected close relationship with program maintainability; debugging and modification expenses are understood to account for a large proportion of software costs over the life of the software. A computable measure of readability would therefore be useful to program developers during coding and to those assuming responsibility for maintenance of software developed elsewhere. In a series of Algol 68 programs, analyzer generated (machine-computable) and human-judged program factors were examined. The first two present authors found that program length and reasonable practice concerning identifier length were excellent predictors of judgments of readability. These predictors were chosen from a large set of analyzer-generated predictors including software science measures as defined by Halstead and several others; the analyzer-generated predictors were found to replicably estimate a high proportion (41 percent) of variance in readability in new readability judgments. Gerrit E. DeYoung, Garry R. Kampen, James M. Topolski |
CHI | 3 |