Bibliographic record
Abstract
We have proposed a new approach to software quality combining cleanroom methodologies and formal methods. Cleanroom emphasizes defect prevention rather than defect removal. Formal methods use mathematical and logical formalizations to find defects early in the software development lifecycle. These two methods have been used separately to improve software quality since the 1980's. The combination of the two methods may provide further quality improvements through reduced software defects. This result, in turn, may reduce development costs, improve time to market, and increase overall product excellence.Defects in computer software are costly. Their detection is usually postponed to the test phase, and their removal is also a very time consuming and expensive task. Cleanroom software engineering is a methodology which relies on preventing the defects, rather than removing them. It is based on incremental development and it emphasizes the development phase. An enhancement to this methodology is presented in this paper, which combines formal methods and cleanroom. The efficiency of the new model rests on an appropriate logical representation, to write the specification of the intended system. In the new model, design plans are formally verified before any implementation is done. The advantages of finding defects in the early stages are decreased cost and increased quality. Results show that, by using formal methods, a higher quality will be achieved and the software project can also benefit from the existing mechanized tools of these two techniques.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".