Identifying Reusable Services in Legacy Object-Oriented Systems: A Type-Sensitive Identification Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The migration of legacy software systems to a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">service-oriented architecture</i> (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 propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ServiceMiner</i>, 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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ServiceMiner</i> relies on a categorisation of service types and code-level patterns characterising types of services. We evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ServiceMiner</i> on four case studies. We also compare our results to those of three state-of-the-art approaches. We show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ServiceMiner</i> identifies architecturally-significant services with, on average, 78% precision, 76% recall, and 77% F-measure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it