SOA ANTIPATTERNS: AN APPROACH FOR THEIR SPECIFICATION AND DETECTION
Bibliographic record
Abstract
Like any other large and complex software systems, Service-Based Systems (SBSs) must evolve to fit new user requirements and execution contexts. The changes resulting from the evolution of SBSs may degrade their design and quality of service (QoS) and may often cause the appearance of common poor solutions in their architecture, called antipatterns, in opposition to design patterns, which are good solutions to recurring problems. Antipatterns resulting from these changes may hinder the future maintenance and evolution of SBSs. The detection of antipatterns is thus crucial to assess the design and QoS of SBSs and facilitate their maintenance and evolution. However, methods and techniques for the detection of antipatterns in SBSs are still in their infancy despite their importance. In this paper, we introduce a novel and innovative approach supported by a framework for specifying and detecting antipatterns in SBSs. Using our approach, we specify 10 well-known and common antipatterns, including Multi Service and Tiny Service, and automatically generate their detection algorithms. We apply and validate the detection algorithms in terms of precision and recall two systems developed independently, (1) Home-Automation, an SBS with 13 services, and (2) FraSCAti, an open-source implementation of the Service Component Architecture (SCA) standard with more than 100 services. This validation demonstrates that our approach enables the specification and detection of Service Oriented Architecture (SOA) antipatterns with an average precision of 90% and recall of 97.5%.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".