John Stephen Davis

dblp:19/4841 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1988
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 first-author

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 · 67% Software maintenance and evolution · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software maintenance and evolution
software complexity
0.011988
A Study of the Applicability of Complexity Measures · IEEE Trans. Software Eng. 1988
Empirical software engineering › software metrics
software complexity metrics
0.011988
A Study of the Applicability of Complexity Measures · IEEE Trans. Software Eng. 1988
Empirical software engineering
software metrics
0.011988
A Study of the Applicability of Complexity Measures · IEEE Trans. Software Eng. 1988

Methods — techniques the papers use, named apart from their topics

empirical study · 0.0chunk-oriented measures · 0.0
YearPublicationVenuePosition
1988 A Study of the Applicability of Complexity Measures
abstract
A study of the predictive value of a variety of syntax-based problem complexity measures is reported. Experimentation with variants of chunk-oriented measures showed that one should judiciously select measurable software attributes as proper indicators of what one wishes to predict, rather than hoping for a single, all-purpose complexity measure. The authors have shown that it is possible for particular complexity measures or other factors to serve as good predictors of some properties of program but not for others. For example, a good predictor of construction time will not necessarily correlate well with the number of error occurrences. M.H. Halstead's (1977) efforts measure (E) was found to be a better predictor that the two nonchunk measures evaluated, namely, T.J. McCabe's (1976) V(G) and lines of code, but at least one chunk measure predicted better than E in every case.>
John Stephen Davis, Richard J. LeBlanc
IEEE Trans. Software Eng.1