SLIMECRAFT: State Learning for Client-Server Regression Analysis and Fault Testing
Bibliographic record
Abstract
In software engineering, behavioral state machine models play a crucial role in validating system behavior and maintaining correctness. This paper proposes an extension of an existing architecture for automatically learning state machine models of client-server systems that automates processes such as regression detection and test case generation, and guides the development of new features. The learned models help identify potential implementation issues of clients, servers, their interactions, as well as the protocols themselves. The architecture also enhances the debugging process and ensures comprehensive system coverage. By employing the LTSDiff algorithm, the method efficiently detects behavioral changes due to software updates, preventing unintended consequences on system performance. Consequently, the automatically generated state machine models can be used as evidence in security, safety, and reliability assurance, providing a valuable tool for development, testing, and maintenance of complex software systems. The learned state machines and detected changes correctly model the behavior of a client-server system to a specified depth at the level of an active outside adversary with the capability to read, replay, replace, or block any message.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".