Visualization and analysis of software engineering data using self-organizing maps

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aut.researcherMacDonell, Stephen Gerard
dc.contributor.authorMacdonell, S
dc.date.accessioned2011-08-05T20:41:47Z
dc.date.accessioned2011-08-06T03:19:29Z
dc.date.available2011-08-05T20:41:47Z
dc.date.available2011-08-06T03:19:29Z
dc.date.copyright2005
dc.date.issued2005
dc.description.abstractThere is no question that accuracy is an important requirement of classification and prediction models used in software engineering management. It is, however, just one of a number of attributes that contribute to a model being 'useful'. Understandably much research has been undertaken with the objective of maximizing model accuracy, but this has often occurred with little regard for these other model attributes, which might include cost-effectiveness, credibility and, for want of a better term, meaningfulness. The research described in this paper addresses both model accuracy and meaningfulness as conveyed by self-organizing maps (SOMs). SOMs are neural-network based representations of data distributions that provide two-dimensional depictions of multi-dimensional relationships. As such they can enable developers and project managers (and researchers) to visualize often complex interactions among and between software measurement data. We illustrate the effectiveness of SOMs by building on two previous empirical studies. Not only are the maps able to portray graphically the distributions of variables and their interrelationships, they also prove to be effective in terms of classification and prediction accuracy. As a result we believe that they could be a useful supplementary tool for researchers and managers concerned with understanding, modeling and controlling complex software projects.
dc.identifier.citationvol.IEEE Catalog 05EX1213C, pages 115 - 124
dc.identifier.doi10.1109/ISESE.2005.1541820
dc.identifier.isbn780395085
dc.identifier.urihttps://hdl.handle.net/10292/1588
dc.publisherIEEE Computer Society Press
dc.relation.replaceshttp://hdl.handle.net/10292/1587
dc.relation.replaces10292/1587
dc.rights©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.rights.accessrightsOpenAccess
dc.subjectAccuracy
dc.subjectData visualization
dc.subjectEngineering management
dc.subjectPredictive models
dc.subjectProject management
dc.subjectSelf organizing feature maps
dc.subjectSoftware development management
dc.subjectSoftware engineering
dc.subjectSoftware measurement
dc.subjectSoftware tools
dc.titleVisualization and analysis of software engineering data using self-organizing maps
dc.typeConference Contribution
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/PBRF Researchers
pubs.organisational-data/AUT/PBRF Researchers/Design & Creative Technologies PBRF Researchers
pubs.organisational-data/AUT/PBRF Researchers/Design & Creative Technologies PBRF Researchers/DCT C & M Computing
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