Seamless Data Migration between Database Schemas with DAMI-Framework: An Empirical Study on Developer Experience
For developers performing data migration during software modernization, this work provides a DSL that reduces cognitive load and error-proneness, though the improvement is incremental over existing model transformation and ETL approaches.
The paper introduces the DAMI-Framework, which includes a domain-specific language (DSL) and parser to simplify data migration between database schemas. An empirical study with 21 developers showed that the DSL reduces migration effort and is perceived as more readable and easier to use than traditional SQL.
Many businesses depend on legacy systems, which often use outdated technology that complicates maintenance and updates. Therefore, software modernization is essential, particularly data migration between different database schemas. Established methodologies, like model transformation and ETL tools, facilitate this migration; they require deep knowledge of database languages and both the source and target schemas. This necessity renders data migration an error-prone and cognitively demanding task. Our objective is to alleviate developers' workloads during schema evolution through our DAMI-Framework. This framework incorporates a domain-specific language (DSL) and a parser to facilitate data migration between database schemas. DAMI-DSL simplifies schema mapping while the parser automates SQL script generation. We assess developer experience in data migration by conducting an empirical evaluation with 21 developers to assess their experiences using our DSL versus traditional SQL. The study allows us to measure their perceptions of the DSL properties and user experience. The participants praised DAMI-DSL for its readability and ease of use. The findings indicate that our DSL reduces data migration efforts compared to SQL scripts.