Survey Research in Software Engineering: Problems and Strategies
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Abstract (arXiv)
Background: The need for empirical investigations in software engineering is growing. Many researchers nowadays, conduct and validate their solutions using empirical research. Survey is one empirical method which enables researchers to collect data from a large population. 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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Apr 2017.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 2.
4 papers.
- 2026Real Faults in Model Context Protocol (MCP) Software: a Comprehensive Taxonomycited by 6
- 2024Exploring the Integration of Large Language Models in Industrial Test Maintenance Processescited by 2
- 2023Analysis of Failures and Risks in Deep Learning Model Converters: A Case Study in the ONNX Ecosystemcited by 0
- 2026A Framework for Using and Evaluating LLMs as Surrogate Experts in Security Surveys: Reliability, Bias, and Implicationscited by 0
- 2026A Framework for Using and Evaluating LLMs as Surrogate Experts in Security Surveys: Reliability, Bias, and Implications6 Jul 2026
- 2026Real Faults in Model Context Protocol (MCP) Software: a Comprehensive Taxonomy5 Mar 2026
- 2024Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes10 Sep 2024
- 2023Analysis of Failures and Risks in Deep Learning Model Converters: A Case Study in the ONNX Ecosystem30 Mar 2023