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Survey Research in Software Engineering: Problems and Mitigation Strategies
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. (SERL)ORCID iD: 0000-0001-9336-4361
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
2018 (English)In: IEEE Access, E-ISSN 2169-3536Article in journal (Refereed) Epub ahead of print
Abstract [en]

Background: The need for empirical investigations in software engineering is growing. Many researchers nowadays, conduct and validate their solutions using empirical research. The Survey is an empirical method which enables researchers to collect data from a large population. The main aim of the survey is to generalize the findings.

Aims: In this study, we aim to identify the problems researchers face during survey design and mitigation strategies.

Method: A literature review, as well as semi-structured interviews with nine software engineering researchers, were conducted to elicit their views on problems and mitigation strategies. The researchers are all focused on empirical software engineering.

Results: We identified 24 problems and 65 strategies, structured according to the survey research process. The most commonly discussed problem was sampling, in particular, the ability to obtain a sufficiently large sample. To improve survey instrument design, evaluation and execution recommendations for question formulation and survey pre-testing were given. The importance of involving multiple researchers in the analysis of survey results was stressed.

Conclusions: The elicited problems and strategies may serve researchers during the design of their studies. However, it was observed that some strategies were conflicting. This shows that it is important to conduct a trade-off analysis between strategies.

Place, publisher, year, edition, pages
IEEE, 2018.
Keywords [en]
Empirical software engineering, surveys
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-17250DOI: 10.1109/ACCESS.2018.2881041OAI: oai:DiVA.org:bth-17250DiVA, id: diva2:1262468
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open access

Available from: 2018-11-12 Created: 2018-11-12 Last updated: 2018-11-29Bibliographically approved

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Petersen, Kai

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89101112131411 of 28
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