Identifying distributed features in SOA by mining dynamic call trees
Bibliographic record
Abstract
Distributed nature of web service computing imposes new challenges on software maintenance community for localizing different software features and maintaining proper quality of service as the services change over time. In this paper, we propose a new approach for identifying the implementation of web service features in a service oriented architecture (SOA) by mining dynamic call trees that are collected from distributed execution traces. The proposed approach addresses the complexities of SOA-based systems that arise from: features whose locations may change due to changing of input parameters; execution traces that are scattered throughout different service provider platforms; and trace files that contain interleaving of execution traces related to different concurrent service users. In this approach, we execute different groups of feature-specific scenarios and mine the resulting dynamic call trees to spot paths in the code of a service feature, which correspond to a specific user input and system state. This allows us to focus on a the implementation of a specific feature in a distributed SOA-based system for different maintenance tasks such as bug localization, structure evaluation, and performance analysis. We define a set of metrics to assess structural properties of a SOA-based system. The effectiveness and applicability of our approach is demonstrated through a case study consisting of two service-oriented banking systems.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".