A Control Flow Representation for Component-Based Software Reliability Analysis
Why this work is in the frame
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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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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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