At What Level of Granularity Should We be Componentizing for Software Reliability?
Bibliographic record
Abstract
In component-based software systems (CBSSs), software designers need to decide about decomposition level (level of granularity) which involves component sizes and the number of components. In these systems, decomposition level is important due to its major impacts on reliability. However, the basis to choose the decomposition level of a CBSS has not been addressed adequately in the existing research. On the other hand, software system components may vary with respect to their criticalities to different failures. The knowledge about component failure criticalities are currently not incorporated in the architectural design decisions of these systems. As a result, these systems consider different failures equally and disregard the various severities of different failures. In this paper, we study the level of decomposition of CBSSs with respect to its impact on their reliabilities based on various component failure criticalities. We discuss the level of decomposition impacts on CBSS architectures with respect to the architectural attributes and component failure criticalities. We derive the reliability of these systems and show the level of decomposition impacts on these system reliabilities.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".