Adaptive Practice on Software Reliability Based on IEEE Std. 1633 in Frequent Requirement Modifications.
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates an adaptive practice on software reliability in frequent requirement modifications. According to the conventional software development processes, software requirements are specified and locked at the early stage of software life cycle. As a project progresses, the requirements can be added and modified to reflect customers needs. However, it can be an obstacle to activities for software reliability engineered process if they are changed frequently. Software is developed in accordance with the requirements. If the frequency of software requirement modifications is high, the software is liable to be error-prone. It also makes the software reliability estimation activities reconfigurable. Therefore, we propose an adaptive approach to estimate software reliability which is based on IEEE Std. 1633.We show why the adaptive approach is necessary when software requirements are changed frequently through a case study.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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