Bibliographic record
Abstract
Antipatterns in Service-based Systems (SBSs)— service antipatterns—represent "bad" solutions to recurring design problems. In opposition to design patterns, which are good solutions, antipatterns should be avoided by the engineers. Antipatterns may also be introduced due to diverse changes performed against new user requirements and execution contexts. Service antipatterns may degrade the quality of design and may hinder the future maintenance and evolution of SBSs. The detection of service antipatterns is important to improve the design quality of SBSs and to ease their maintenance. A better understanding of service antipatterns is a must prerequisite to perform their detection. This paper presents a taxonomy of service antipatterns in Web services and SCA (Service Component Architecture), the two common SBSs implementation technologies. The presented taxonomy will facilitate engineers their understanding on service antipatterns. Other substantial benefits of the presented taxonomy include: (1) assisting in the specification and detection of service antipatterns, (2) revealing the relationships among various groups of service antipatterns, (3) grouping together antipatterns that are fundamentally related, and (4) providing an overview of various system-level design problems ensemble.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".