White-Box and Black-Box Reliability Modeling Framework: Integration Through Analytical Model and User Profile Validation via Deep Learning — A Practitioner’s Approach
Bibliographic record
Abstract
Software reliability evaluation of complex systems is always a challenging task with conventional methods comprising both functional as well as nonfunctional aspects of real-world applications. Prevailing model frameworks moreover apply a nonfunctional approach (black-box model) that is modeled on defect data or through a functional approach (white-box model) that uses component or state-based interactions. Also, other challenges involve integrating both approaches, and validating user profiles of software operation. Further, reliability assessment is one among the most important and desirable qualities of service requirements of software systems, particularly in monitoring critical business transactions. Here, we propose a model framework to evaluate the overall reliability estimation involving both functional and nonfunctional model analyses using: (a) white-box assessment based on intercomponent analysis via component-based Cheung’s model and user profile validations with one of the identified deep learning techniques and (b) black-box modeling evaluation via generalized stochastic Petri nets based on orthogonal defect classification. A newly introduced deep learning model using white-box analysis is validated with pertinent usage profiles to establish a new trend in artificial neural networks and as well with software reliability estimation. Additionally, we introduce and present a quantitative technique — analytical hierarchy process — to integrate reliability assessment and provide weights to the white-box and as well for black-box approaches to quantify overall reliability estimation. The proposed framework is illustrated with an application case study.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".