On the Migration of Legacy Systems to an Event-Driven Architecture: A Survey
Bibliographic record
Abstract
Legacy systems still play a critical role in the operation of many software organizations. However, these systems have high maintenance costs due to their reliance on deprecated technologies. Also, they are often too complex to be rewritten from scratch. Such systems could benefit from being migrated to an event-driven architecture (EDA), as it enables the creation of software systems with loosely coupled components which improves their maintainability and scalability. However, migrating legacy systems is not straightforward, and may have significant costs. Furthermore, there is limited empirical knowledge on how practitioners approach legacy-to-EDA migration in industrial settings. To bridge this gap, we conducted a survey with software practitioners to investigate the state of practice of legacy-to-EDA migration. The purpose of the survey is to gain insights on the methodologies adopted for implementing EDAs, and the specifics of the legacy-to-EDA migration process in industrial settings. The survey consists of two parts: (1) an online questionnaire featuring 26 questions, and (2) an interview session with some of the participants. The questionnaire was answered by 31 participants, two of whom volunteered for the interview. The key findings of the survey include: (1) the main motivation behind legacy-to-EDA migrations is to decouple parts of the system, (2) the technologies preferred by professionals when implementing EDAs are Java and Apache Kafka, and (3) software professionals mainly rely on business processes and human expertise to guide the migration. Our study highlights the limited adoption of automation tools in the legacy-to-EDA migration process. It also emphasizes the need for practitioners to develop tools that facilitate the migration and to adopt best practices to handle data consistency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".