Evaluating Software Quality Metrics for Enhanced Software Management and Engineering
Bibliographic record
Abstract
In software, competition in producing high-quality products has become a prominent factor for business success.In this regard, identifying and defining software quality metrics (SQM) to discover and continuously enhance current quality systems is very important.However, it is advisable to study and review current studies in this field, so that it is possible to analyze the current situation, and it also enables us to formulate expectations regarding future research areas.This research is concerned with studying and analyzing a large number of articles, focusing on the research literature published over the past decade.70 research papers, articles, and conference papers were selected and analyzed, published from 2009 to 2023.A detailed description of these researches and their titles SQM was conducted.We used graphics, explanations, and structure design to display the results.The outputs from this research indicate the underlying knowledge in this field and the measurement mechanism and include trends between 2009 and 2023 and the gaps that are supposed to be available for study and development in this field.The study and analysis of articles aim to review studies, direct future studies, and focus on system development.Future studies encourage the adoption of quality metrics.Quality metrics include several areas of development systems, including network performance, and the Cloud of Things, which directs the adoption of more accurate metrics and components reusability, artificial intelligence, model performance, and predictive capacity metrics.
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.
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 | 0.028 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".