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W. K. Cheung

dblp:38/6589 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1987
—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 · 67% Program analysis · 33%

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

TopicWeightPapersLastEvidence papers
Empirical software engineering › software metrics
software complexity metrics
0.011987
An Empirical Study of Software Metrics · IEEE Trans. Software Eng. 1987
Empirical software engineering
software metrics
0.011987
An Empirical Study of Software Metrics · IEEE Trans. Software Eng. 1987
Program analysis
static analysis
0.011987
An Empirical Study of Software Metrics · IEEE Trans. Software Eng. 1987

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

static source code analysis · 0.0flowgraph analysis · 0.0correlation analysis · 0.0
YearPublicationVenuePosition
1987 An Empirical Study of Software Metrics
abstract
Software metrics are computed for the purpose of evaluating certain characteristics of the software developed. A Fortran static source code analyzer, FORTRANAL, was developed to study 31 metrics, including a new hybrid metric introduced in this paper, and applied to a database of 255 programs, all of which were student assignments. Comparisons among these metrics are performed. Their cross-correlation confirms the internal consistency of some of these metrics which belong to the same class. To remedy the incompleteness of most of these metrics, the proposed metric incorporates context sensitivity to structural attributes extracted from a flow graph. It is also concluded that many volume metrics have similar performance while some control metrics surprisingly correlate well with typical volume metrics in the test samples used. A flexible class of hybrid metric can incorporate both volume and control attributes in assessing software complexity.
Hon Fung Li, W. K. Cheung
IEEE Trans. Software Eng.2