A Control Flow Representation for Component-Based Software Reliability Analysis
Bibliographic record
Abstract
Current reliability analysis techniques encounter a prohibitive challenge with respect to the control flow representation of large software systems with intricate control flow structures. Some techniques use a component-based Control Flow Graph (CFG) structure which represents only inter-component control flow transitions. This CFG structure disregards the dependencies among multiple outward control flow transitions of a system component and does not provide any details about a component internal control flow structure. To overcome these problems, some techniques use statement-based or block-based CFGs. However, these CFG structures are remarkably complex and difficult to use for large software systems. In this paper, we propose a simple CFG structure called Connection Dependency Graph (CDG) that represents inter-component and intra-component control flow transitions and preserves the dependencies among them. We describe the CDG structure and explain how to derive it from a program source code. Our derivation exploits a number of architectural patterns to capture the control flow transitions and identify the execution paths among connections. We provide a case study to examine the effect of program size on the CDG, the statement-based, and the block-based CFGs by comparing them with respect to complexity using the PostgreSQL open source database system.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".