Performance aware software development (PASD) using resource demand budgets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Performance Aware Software Development (PASD) as described here combines a software specification, a model, and resource demand budgets. The budgets are planning figures created by the designers and managers, from the requirements and their experience. The key elements of this approach are the planning of budgets for the resource demands of each of the parts and operations of the system, and a validation check (using the model) for the required performance. The paper starts from a Use Case Map (UCM) specification, but other specification languages such as UML could equally be used. Demand budgets are allocated to responsibilities and the entire budget is verified by a semi-automated performance analysis using Layered Queuing Network (LQN) models. The key step is to add "completions" to the software system design, representing those parts of the system not defined in the software specification (infrastructure such as middleware, the environment, and competing applications), which could impact the performance. Budget adjustments are indicated by bottleneck locations and the sensitivity of results.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it