Recovering business processes from business applications
Bibliographic record
Abstract
Abstract A business process, such as the process followed when ordering a book, describes the order of executing tasks (e.g., check inventory, verify credit card, and ship book). Business applications implement the business processes for the daily operations of an organization. Organizations must continuously modify their business applications to accommodate changes to business processes. However, business applications are often designed and developed without referring to the documented definitions of business processes. Modifying business applications is a time‐consuming and error‐prone task. To correctly perform this task, developers require an in‐depth understanding of multi‐tiered applications and the definitions of the business processes that they implement. In this paper, we present an approach that automatically recovers business process definitions from multi‐tiered business applications. Given the starting UI screen of a particular business process, the approach recovers the process definition by tracing the flow of control throughout the different tiers of the business application. We demonstrate the effectiveness of our approach through a case study using 15 business applications from three large open‐source projects. Our case study demonstrates that our approach can recover business process definitions from the implementation with high precision and recall. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".