Improving the Modifiability of the Architecture of Business Applications
Bibliographic record
Abstract
In the current rapidly changing business environment, organizations must keep on changing their business applications to maintain their competitive edges. Therefore, the modifiability of a business application is critical to the success of organizations. Software architecture plays an important role in ensuring a desired modifiability of business applications. However, few approaches exist to automatically assess and improve the modifiability of software architectures. Generally speaking, existing approaches rely on software architects to design software architecture based on their experience and knowledge. In this paper, we build on our prior work on automatic generation of software architectures from business processes and propose a collection of model transformation rules to automatically improve the modifiability of software architectures. We extend a set of existing product metrics to assess the modifiability impact of the proposed model transformation rules and guide the quality improvement process. Eventually, we can generate software architecture with desired modifiability from business processes. We conduct a case study to illustrate the effectiveness of our transformation rules.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".