From run-time behavior to usage scenarios
Bibliographic record
Abstract
A key challenge facing IT organizations today is their evolution towards adopting e-business practices that gives rise to the need for reengineering their underlying software systems. Any reengineering effort has to be aware of the functional requirements of the subject system, in order not to violate the integrity of its intended uses. However, as software systems get regularly maintained throughout their lifecycle, the documentation of their requirements often become obsolete or get lost. To address this problem of "software requirements loss", we have developed an interaction-pattern mining method for the recovery of functional requirements as usage scenarios. Our method analyzes traces of the run-time system-user interaction to discover frequently recurring patterns; these patterns correspond to the functionality currently exercised by the system users, represented as usage scenarios. The discovered scenarios provide the basis for reengineering the software system into web-accessible components, each one supporting one of the discovered scenarios. In this paper, we describe IPM2, our interaction-pattern discovery algorithm, we illustrate it with a case study from a real application and we give an overview of the reengineering process in the context of which it is employed.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".