Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

James M. Topolski

dblp:297/3082 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Software maintenance and evolution
code readability
0.011982
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
YearPublicationVenuePosition
1982 Analyzer-generated and human-judged predictors of computer program readability
abstract
The 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
CHI3