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Applying clustering to analyze opinion diversity
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (SERG)
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (SERG)
2015 (English)In: Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering / [ed] He (Jason) Zhang, New York: Association for Computing Machinery (ACM), 2015Conference paper, Published paper (Refereed)
Abstract [en]

In empirical software engineering research there is an increased use of questionnaires and surveys to collect information from practitioners. Typically, such data is then analyzed based on overall, descriptive statistics. Even though this can capture the general trends there is a risk that the opinions of different (minority) sub-groups are lost. Here we propose the use of clustering to segment the respondents so that a more detailed analysis can be achieved. Our findings suggest that it can give a better insight about the survey population and the participants' opinions. This partitioning approach can show more precisely the extent of opinion differences between different groups. This approach also gives an opportunity for the minorities to be heard. Through the process significant new findings may also be obtained. In our example study regarding the state of testing and requirement activities in industry, we found several significant groups that showed significant opinion differences from the overall conclusion.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2015.
Keyword [en]
Empirical Survey, Clustering, Data Mining, Partitioning, Grouping, Diversity, Minority, Expert Opinion
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-35985DOI: 10.1145/2745802.2745809ISBN: 978-1-4503-3350-4 (print)OAI: oai:DiVA.org:kau-35985DiVA, id: diva2:810874
Conference
EASE 2015 - 19th International Conference on Evaluation and Assessment in Software Engineering, Nanjing (China), 27-29 April 2015
Projects
EU: strukturfonder, projekt Compare Business Innovation Centre (CBIC III)KKS projekt 20130085: Testing of Critical System Characteristics (TOCSYC)
Funder
Knowledge FoundationEU, European Research Council
Available from: 2015-05-08 Created: 2015-05-08 Last updated: 2017-01-25Bibliographically approved

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Blom, MartinHassan, Mohammad Mahdi

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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