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Published in:
2025 | OriginalPaper | Chapter
Generating and Evolving Real-Life Like Synthetic Data for e-Government Services Without Using Real-World Raw Data
Authors : Maj-Annika Tammisto, Dietmar Pfahl, Faiz Ali Shah
Published in: Product-Focused Software Process Improvement. Industry-, Workshop-, and Doctoral Symposium Papers
Publisher: Springer Nature Switzerland
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Abstract
Testing of applications that use data from e-Government services as input requires test data that is real-life like but where the privacy of personal information is guaranteed. Many approaches exist for creating high-quality synthetic test data, but most of them need real-life raw data as input. Our research aims to develop and evaluate an approach for generating and evolving real-life like synthetic test data without using real-world raw data. The expected benefit of our research is to enable the creation and evolvement of meaningful and real-life like synthetic test data in situations where real-life raw data is not accessible due to privacy reasons.
