A Connection-Based Signature Approach for Control Flow Error Detection
Bibliographic record
Abstract
Control Flow Errors (CFEs) are major impairments of software system correctness. These CFEs can be caused by operational faults with respect to the execution environment of a software system. Several techniques are proposed to monitor the control flow using signature-based approaches. These techniques partition a software program into branch-free blocks and assign a unique signature for each block. They detect CFEs by comparing the runtime signatures of these blocks with pre-computed signatures based on the program Control Flow Graph (CFG). Unfortunately, branch-free block partitioning does not completely include all the program connections. Consequently, these techniques may fail to detect some invalid transitions due to lack of signatures associated with those missing connections. In this paper, we propose a connection-based signature approach for CFE detection. We first describe our connection-based signature structure in which we partition the program components into Connection Implementation Blocks (CIBs). Each CIB is associated with a Connection-based CFG (CCFG) to represent the control structure of its code segment. We present our control flow monitor structure and CFE checking algorithm using these CCFGs. The error detection approach is evaluated using PostgreSQL open-source database. The results show that this technique is capable of detecting CFEs in different software versions with variable numbers of randomly injected faults.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".