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Colin Connaughton

dblp:37/4217 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2006
—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 · 100%

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

TopicWeightPapersLastEvidence papers
Empirical software engineering › software metrics
goal-question-metric
0.112006
Lessons learnt from the analysis of large-scale corporate databases · ICSE 2006
Empirical software engineering
software metrics
0.112006
Lessons learnt from the analysis of large-scale corporate databases · ICSE 2006

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

m3p · 0.1entity-relationship modeling · 0.1GQM · 0.1
YearPublicationVenuePosition
2006 Lessons learnt from the analysis of large-scale corporate databases
abstract
This paper presents the lessons learnt during the analysis of the corporate databases developed by IBM Global Services (Australia). IBM is rated as CMM level 5. Following CMM level 4 and above practices, IBM designed several software metrics databases with associated data collection and reporting systems to manage its corporate goals. However, IBM quality staff believed the data were not as useful as they had expected. NICTA staff undertook a review of IBM's statistical process control procedures and found problems with the databases mainly due to a lack of links between the different data tables. Such problems might be avoided by using M3P variant of the GQM paradigm to define a hierarchy of goals, with project goals at the lowest level, then process goals and corporate goals at the highest level. We propose using E-R models to identify problems with existing databases and to design databases once goals have been defined.
Barbara A. Kitchenham, Cat Kutay, D. Ross Jeffery, Colin Connaughton
ICSE4