Software Quality Assessment Algorithm Based on Fuzzy Logic
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
In this paper an attempt has been made to provide a new global evaluation approach of a specified software quality model extracted from a generic software quality model using an instantiation procedure. The evaluation is based on data extracted from an ambient distributed system composed of fusion and fission agents connected to input/output services. These data are linked to the appropriate metrics of our software quality model and we use quality factors stated in ISO standards and different models of researchers represented under an ontology. We use equivalent relations to link criteria that have the same meaning and fuzzy logic approach to evaluate the entire software quality model. Our work presents the following contributions: (i) creating a generic software quality model based on several existing software quality standards and formalized under ontology concepts (ii) proposing an instantiation algorithm to extract specified software quality model from a generic software quality models (iii) proposing a new global evaluation approach of the specified software quality model using two processes, the first one executes metrics related to sensors data and the second one uses the result of the first process using fuzzy logic approach evaluating the entire specified software quality model and end up with a final numerical result (iv) adding the variability of metric variables algorithm to determine the impact of a possible variation of one criterion on others and avoid their penalization. This can help to conduct a trade-off-analysis in the proposed quality evaluation approach.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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