Identifying Reusable Services in Legacy Object-Oriented Systems: A Type-Sensitive Identification Approach
Bibliographic record
Abstract
The migration of legacy software systems to aservice-oriented architecture(SOA) is one of the main strategies for modernising such systems. The success of modernising a legacy system to a SOA highly depends on the used service identification approach where the goal is to identify reusable functionalities that could become services. In this paper, we perform a comparative analysis of service identification approaches proposed by academia and industry. We show that there is a gap between academia and industry in the used approaches to identify services from legacy systems. We extract from the comparative analysis several recommendations about the inputs, processes, and outputs that a service identification approach should have. Based on these recommendations, we proposeServiceMiner, a bottom-up service identification approach, which relies on source-code analysis, because other sources of information may be unavailable or out of sync with the actual code.ServiceMinerrelies on a categorisation of service types and code-level patterns characterising types of services. We evaluateServiceMineron four case studies. We also compare our results to those of three state-of-the-art approaches. We show thatServiceMineridentifies architecturally-significant services with, on average, 78% precision, 76% recall, and 77% F-measure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".