Timed Wp-method: testing real-time systems
Bibliographic record
Abstract
Real-time systems interact with their environment using time constrained input/output signals. Examples of real-time systems include patient monitoring systems, air traffic control systems, and telecommunication systems. For such systems, a functional misbehavior or a deviation from the specified time constraints may have catastrophic consequences. Therefore, ensuring the correctness of real-time systems becomes necessary. Two different techniques are usually used to cope with the correctness of a software system prior to its deployment, namely, verification and testing. In this paper, we address the issue of testing real-time software systems specified as a timed input output automaton (TIOA). TIOA is a variant of timed automaton. We introduce the syntax and semantics of TIOA. We present the potential faults that can be encountered in a timed system implementation. We study these different faults based on TIOA model and look at their effects on the execution of the system using the region graph. We present a method for generating timed test cases. This method is based on a state characterization technique and consists of the following three steps: First, we sample the region graph using a suitable granularity, in order to construct a subautomaton easily testable, called grid automaton. Then, we transform the grid automaton into a nondeterministic timed finite state machine (NTFSM). Finally, we adapt the generalized Wp-method to generate timed test cases from NTFSM. We assess the fault coverage of our test cases generation method and prove its ability to detect all the possible faults. Throughout the paper, we use examples to illustrate the various concepts and techniques used in our approach.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".