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M. A. Borst

dblp:73/5044 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1979
—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
Software maintenance and evolution · 75% Empirical software engineering · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software maintenance and evolution
program comprehension
0.011979
Measuring the Psychological Complexity of Software Maintenance Tasks with the Halstead and McCabe Metrics · IEEE Trans. Software Eng. 1979
Software maintenance and evolution
software complexity
0.011979
Measuring the Psychological Complexity of Software Maintenance Tasks with the Halstead and McCabe Metrics · IEEE Trans. Software Eng. 1979
Empirical software engineering
software metrics
0.011979
Measuring the Psychological Complexity of Software Maintenance Tasks with the Halstead and McCabe Metrics · IEEE Trans. Software Eng. 1979
Software maintenance and evolution › software maintenance
software modification
0.011979
Measuring the Psychological Complexity of Software Maintenance Tasks with the Halstead and McCabe Metrics · IEEE Trans. Software Eng. 1979

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

correlation analysis · 0.0controlled experiment · 0.0
YearPublicationVenuePosition
1979 Measuring the Psychological Complexity of Software Maintenance Tasks with the Halstead and McCabe Metrics
abstract
Three software complexity measures (Halstead's E, McCabe's u(G), and the length as measured by number of statements) were compared to programmer performance on two software maintenance tasks. In an experiment on understanding, length and u(G) correlated with the percent of statements correctly recalled. In an experiment on modification, most significant correlations were obtained with metrics computed on modified rather than unmodified code. All three metrics correlated with both the accuracy of the modification and the time to completion. Relationships in both experiments occurred primarily in unstructured rather than structured code, and in code with no comments. The metrics were also most predictive of performance for less experienced programmers. Thus, these metrics appear to assess psychological complexity primarily where programming practices do not provide assistance in understanding the code.
Bill Curtis, Sylvia B. Sheppard, Phil Milliman, M. A. Borst, Tom Love
IEEE Trans. Software Eng.4