Interactive Refactoring of Web Service Interfaces Using Computational Search
Bibliographic record
Abstract
Successful Web services evolve through a process of continuous change due to several reasons such as improving the quality, fixing bugs and adding new features. However, this evolution process may weaken the design of the Web service's interface by aggregating many non-cohesive and semantically unrelated operations. Thus, the service interface becomes unnecessarily complex for users to find relevant operations to be used by their services-based systems. In this paper, we propose an interactive recommendation approach, based on evolutionary algorithms, that dynamically adapts and suggests a possible remodularization of the Web services interface design to users/developers and takes their feedback into consideration. Our approach uses an interactive multi-criteria decision-making algorithm, based on interactive Non-dominated Sorting Genetic Algorithm (NSGA-II), to find a set of good design interface modularization solutions. These solutions provide a trade-off between improving several interface design quality metrics (e.g., coupling, cohesion, number of port types, and number of antipatterns) and fix Web services design antipatterns, maximizing the satisfaction of the interaction constraints learnt from the user feedback during the execution of the algorithm while minimizing the deviation from the initial design. We evaluated our approach on a set of 22 real world Web services, provided by Amazon and Yahoo. Statistical analysis of our experiments shows that our dynamic interactive Web services interface modularization approach performed significantly better than the state-of-the-art modularization techniques in terms of generating well-designed Web services interface for users.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".