A Study On Automatic Software Quality And Reliability Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this thesis the study over the topic of analytical approaches for software quality and reliability assurance is presented. The focus of this research is on a specific set of techniques used for software reliability assessment called Risk Analysis. Numerous approaches are explored and different new techniques are proposed to generate the risk model of a software product. These techniques are evaluated and using the results of this evaluation a new risk model (Compound Risk Model) is proposed which is using the advantages of different classes of risk analysis techniques to generate a more precise and practical model to identify more risky components of a software product. Also a research on the topic of Automatic Bug-Fix using Genetic Programming is presented which can fix logical defects of a buggy code and evolve it to a bug-free code. Finally it is discussed that these approaches can be used as an automated tool in an integrated development environment to localize the defective components and debug them.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it