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Robert T. Hughes

dblp:00/1674 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2010
—ORCID · none

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

Software engineering, systems software and programming languages · 2 · 1 first-authorDatabases, data management, data science and information retrieval · 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 · 100%
Databases, data mining, and information retrieval
1 paper
Data models and query languages · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Empirical software engineering
software metrics
0.012001
Modeling Software Measurement Data · IEEE Trans. Software Eng. 2001
Data models and query languages
entity-relationship model
0.012001
Modeling Software Measurement Data · IEEE Trans. Software Eng. 2001

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

entity-relationship modeling · 0.1
YearPublicationVenuePosition
2010 Ontological Modelling to Support the Planning of is Development Processes - A Position Paper
Robert T. Hughes, Graham Winstanley, Karl Cox
KEOD1
2001 Modeling Software Measurement Data
abstract
This paper proposes a method for specifying models of software data sets in order to capture the definitions and relationships among software measures. We believe a method of defining software data sets is necessary to ensure that software data are trustworthy. Software companies introducing a measurement program need to establish procedures to collect and store trustworthy measurement data. Without appropriate definitions it is difficult to ensure data values are repeatable and comparable. Software metrics researchers need to maintain collections of software data sets. Such collections allow researchers to assess the generality of software engineering phenomena. Without appropriate safeguards, it is difficult to ensure that data from different sources are analyzed correctly. These issues imply the need for a standard method of specifying software data sets so they are fully documented and can be exchanged with confidence. We suggest our method of defining data sets can be used as such a standard. We present our proposed method in terms of a conceptual entity-relationship data model that allows complex software data sets to be modeled and their data values stored. The standard can, therefore, contribute both to the definition of a company measurement program and to the exchange of data sets among researchers.
Barbara A. Kitchenham, Robert T. Hughes, Stephen G. Linkman
IEEE Trans. Software Eng.2
1996 Expert judgement as an estimating method
Robert T. Hughes
Inf. Softw. Technol.1