Expected software quality profile: A methodology and a case study
Bibliographic record
Abstract
For decades, the notion of software quality evaluation is raised as a challenging task. Recently many studies have presented quality evaluation methodologies for specific domains or specific techniques. They usually select a pre-defined model, customize the characteristics, define the metrics and evaluate the quality of the product or development process. Our study presents a bottom-up methodology for the quality evaluation process. In this paper, we present a methodology to create the expected quality profile. In our approach, the first step is listening to the users, and then retrieving the most important quality factors and creating a model to evaluate the expected quality of the software product. The profile is formed by eliciting the expected users' quality expectations, and then quantifying the elicited factors by applying them to our quality evaluation model and the ISO/IEC 25000 standard. The result of this research empowers the software development stakeholders to perform a crosscheck between users' specific quality expectations and other drivers (functional and architecture/design requirements), before or during the software development process. The crosscheck aims to guarantee that there are enough activities, roles and artifacts in the software development process to support the users' quality requirements.
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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".