Software error early detection system based on run-time statistical analysis of function return values
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Large software systems are extremely complex and based on code that is constantly changing with bug fixes and new features. As a result, these systems will likely never be free of bugs. The bugs typically don't expose themselves until they are triggered by a new workload, and when triggered, they are rarely immediately fatal, but result in a system that continues to run with corrupt internal state, deteriorating over time to the point where it becomes inoperable. Having a method to identify corrupt state early would allow the initiation of defensive actions such as flushing page caches or redirecting external requests to another service in the cluster. In this paper, we propose a statistical method of detecting problems in software at run-time based on analyzing function return values. The methodology, at this time, requires the availability of source code, but does not require understanding the source code. Our experimental results indicate that our method can be effective in identifying problems early on, potentially allowing for defensive measures. The overhead is negligible at less than 1%. 1
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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