Supporting Change Impact Analysis for Service Oriented Business Applications
Bibliographic record
Abstract
Business applications encode various business processes within an organization. Business process specification languages such as BPEL (Business Process Execution Language) are commonly used to integrate various services in order to automate business processes within an organization. To remain competitive edge, managers frequently modify their processes. Determining the cost of modifying a business process is not trivial since the changes to the business process have to account for source code changes in various services. In this paper, we propose an approach to estimating the cost of a business process change in a service oriented business application. The approach applies change impact analysis techniques to business process specifications, and source code. The approach generates an initial change impact set from business process components. These components are then mapped to the corresponding source code entities. These code entities act as seeds for traditional source code impact analysis. Using code dependencies, such as call and inheritance relations, we derive a metric to capture the complexity of particular business process changes. Managers can then use this metric to gauge the cost and resources needed to implement changes in their business processes without having to study the code. We demonstrated the feasibility of our approach using an experiment on an open source service oriented business application.
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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".