Modeling and Selecting Frameworks in Terms of Patterns, Tactics and System Qualities
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
Selecting a framework and documenting the rationale for choosing it is an essential task for system architects. Different framework selection approaches have been proposed in the literature. However, none of these connect frameworks to qualities based on their implemented patterns and tactics. In this paper, we propose a way to semi-automatically compare the quality attributes of frameworks by extracting the patterns and tactics from a framework’s source code and documenting them to connect frameworks to requirements upon which a selection can be made. We use a tool called Archie (a tool used to extract tactics from a Java-based system’s code) to extract the patterns/tactics from the implementation code of frameworks. We then document and model these patterns/tactics and their impact on qualities using the Goal-oriented Requirements Language (GRL). After that, we reuse these models of patterns and tactics to model frameworks in terms of their implemented patterns and tactics. The satisfaction level of the quality requirements integrated with other criteria such as the preferences of an architect provide architects with a tool for comparing different frameworks and documenting their rationale for choosing a framework. As a validation of the approach, we apply it to three realistic case studies with promising 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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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