Slicify: Fault Injection Testing for Network Partitions
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
Modern distributed systems are complex. They include hundreds of components that implement complex protocols such as scheduling, replication, and access control. These systems are expected to offer high availability and preserve their data even in the face of external environmental faults. Testing is the primary approach for improving system reliability. Testing against environmental faults such as hardware failures, memory corruption, and network problems is complicated since they can happen at any step in the protocol and affect any component.We present Slicify, a generic framework to test the network partition resilience of distributed systems. Slicify injects network partitions during unit tests to analyze system behavior in their presence. Slicify reduces the test space in an application-agnostic fashion with its novel connection tracking mechanism. We verify Slicify’s capabilities by reproducing previously documented failures in two production systems. In addition, we demonstrate its effectiveness by uncovering new failures in three popular distributed systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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