Development and configuration of service-oriented systems families
Bibliographic record
Abstract
Software Product Lines (SPLs) are families of software systems which share a common sets of feature and are developed through common set of core assets in order to promotes software reusability, mass customization, reducing cost, time-to-market and improving the quality of the product. SPLs are sets (i.e., families) of software applications developed as a whole for a specific business domain. Particular applications are derived from software families by selecting the desired features through configuration process. Traditionally, SPLs are implemented with systematically developed components, shared by members of the SPLs and reused every time a new application is derived. In this paper, we propose an approach to the development and configuration of Service-Oriented SPLs in which services are used as reusable assets and building blocks of implementation. Our proposed approach also suggests prioritization of family features according to stakeholder's non-functional requirements (NFRs) and preferences. Priorities of NFRs are used to filter the most important features of the family, which is performed by Stratified Analytic Hierarchical Process (S-AHP). The priorities also are used further for the selection of appropriate services implementation for business processes realizing features. We apply Mixed Integer Linear Programming to find the optimal service selection within the constraints boundaries specified by stakeholders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".