Software Quality Assessment Technique for the Autonomous Power Plants Automated Control Systems
Bibliographic record
Abstract
The paper proposes further development of formal method for obtaining the values of software quality attributes and assessing the quality of software used for controlling and parameters monitoring of autonomous electric power systems.For this purpose, dynamic software testing was used with the involvement of a group of experts.The results of the conducted expert assessment were used as initial data.The attributes of software quality indicators, such as functionality, practicality, maintainability, reliability, were calculated using the method of summarizing and grouping the results of statistical observation that allowed to check the software compliance with quality standards.Additional weighting coefficients that describe the importance of individual software quality attributes were introduced, and additive convolution is used to calculate the values of various software quality indicators.Minimax criterion was used to find the best solution that maximizes the quality of the software and minimizes possible losses due to errors.The technique proposed in the paper makes it possible to obtain quantitative assessments of software quality based on statistical processing of testing results and expert assessments.This allows to select specific software characteristics for improvement without affecting others, to predict software failureless time and to minimize the subjective factor during testing.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".