A Hybrid Approach for Improving the Design Quality of Web Service Interfaces
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Bibliographic record
Abstract
A key success of a Web service is to appropriately design its interface to make it easy to consume and understand. In the context of service-oriented computing (SOC), the service’s interface is the main source of interaction with the consumers to reuse the service functionality in real-world applications. The SOC paradigm provides a collection of principles and guidelines to properly design services to provide best practice of third-party reuse. However, recent studies showed that service designers tend to pay little care to the design of their service interfaces, which often lead to several side effects known as antipatterns . One of the most common Web service interface antipatterns is to expose a large number of semantically unrelated operations, implementing different abstractions, in one single interface. Such bad design practices may have a significant impact on the service reusability, understandability, as well as the development and run-time characteristics. To address this problem, in this article, we propose a hybrid approach to improve the design quality of Web service interfaces and fix antipatterns as a combination of both deterministic and heuristic-based approaches. The first step consists of a deterministic approach using a graph partitioning-based technique to split the operations of a large service interface into more cohesive interfaces, each one representing a distinct abstraction. Then, the produced interfaces will be checked using a heuristic-based approach based on the non-dominated sorting genetic algorithm (NSGA-II) to correct potential antipatterns while reducing the interface design deviation to avoid taking the service away from its original design. To evaluate our approach, we conduct an empirical study on a benchmark of 26 real-world Web services provided by Amazon and Yahoo. Our experiments consist of a quantitative evaluation based on design quality metrics, as well as a qualitative evaluation with developers to assess its usefulness in practice. The results show that our approach significantly outperforms existing approaches and provides more meaningful results from a developer’s perspective.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 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