Automated SQA Framework with Predictive Machine Learning in Airfield Software
Bibliographic record
Abstract
Given the intricate composition and complex nature of modern software systems, it is necessary to ensure sufficient software quality throughout their entire life cycle. This paper highlights the development efforts made toward delivering an automated solution for software quality metric acquisition and the analysis of quality-related data for a real-world airfield operations software. The target software system, at the time of producing this paper, consists of over 110 K lines of code, requires over 10 K developer minutes to address quality issues, contains over 140 identified bugs, has approximately 50 security hotspots, and includes nearly 3 K code smells. Considering the abundance of quality-related items uncovered by the solution being developed, the airfield software was presented as an exemplary case study. This paper introduces a novel dual-framework architecture for software quality assurance that specifically targets the airfield software system in focus. This unique approach combines data logging for metric acquisition and machine learning for predictive analysis. This helps address real-time operations, integration challenges, and security concerns in the target software. This paper highlights the tools and technologies selected, the architecture implementing the frameworks and processes used, and the results of preliminary experiments and analysis activities.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".