arXiv subjects

Jan Tretmans

Publications and source records attributed to Jan Tretmans.

4 recordsLinked to original sources

arXiv2026

Scenario-Driven Neuroevolution: Using Models to Guide Test Generation for Games

Automatically generating test inputs for games is challenging, as test generators must master the game to reach advanced program states while also ensuring robustness against the heavy program randomisation inherent to games. The test generator Neatest therefore optimises test suites consisting of neural networks that reach advanced program states and are robust to program randomisation, as they generate test inputs dynamically based on the current program state. Neatest is a white-box testing approach that aims to generate a network agent for each yet-uncovered statement or branch of the code using neuroevolution. Due to this iterative test generation approach, the algorithm does not scale well to larger programs that may contain thousands of branches. Furthermore, covering every statement or branch in a game often does not correspond to playing the game as intended. To alleviate these shortcomings, we propose combining Neatest with a model-based testing approach that allows game testers to define test scenarios via abstract game models. The test generator then no longer optimises networks to reach all branches or statements of a program, but instead trains networks to replicate the concrete desired testing behaviour expressed by the abstract game model. An evaluation on 13 Scratch games across varying genres demonstrates that Neatest, combined with model-based testing, is able to optimise agents that replicate the desired gameplay behaviour defined in the game models while increasing achieved branch coverage by 7% compared to the traditional code-guided Neatest approach.

cs.SE↗

arXiv2025

Testing Compositionality

Compositionality supports the manipulation of large systems by working on their components. For model-based testing, this means that large systems can be tested by modelling and testing their components: passing tests for all components implies passing tests for the whole system. In previous work, we defined mutual acceptance for specification models and proved that this property is a sufficient condition for compositionality in model-based testing. In this paper, we present three main algorithms for using mutual acceptance in practice. First, we can verify mutual acceptance on specifications, proving compositionality for all valid implementations. Second, we give a sound and exhaustive model-based testing procedure which checks mutual acceptance on a specific black-box implementation. The result is that testing the correctness of large systems can be decomposed into testing the component implementations for uioco conformance to their specifications, and testing for environmental conformance to the specifications of their environment. Finally, we optimise this procedure further by utilizing the constraints imposed by multiple specifications at the same time. These three algorithms together allow picking the most suitable approach for a given situation, trading in more generalizable results for faster runtime by optimising for a specific context as desired.

cs.SE↗

arXiv2023

Compositionality in Model-Based Testing

Model-based testing (MBT) promises a scalable solution to testing large systems, if a model is available. Creating these models for large systems, however, has proven to be difficult. Composing larger models from smaller ones could solve this, but our current MBT conformance relation $\textbf{uioco}$ is not compositional, i.e. correctly tested components, when composed into a system, can still lead to a faulty system. To catch these integration problems, we introduce a new relation over component models called $\textbf{mutual acceptance}$. Mutually accepting components are guaranteed to communicate correctly, which makes MBT compositional. In addition to providing compositionality, mutual acceptance has benefits when retesting systems with updated components, and when diagnosing systems consisting of components.

cs.SE↗

arXiv2019

Relating Alternating Relations for Conformance and Refinement

Various relations have been defined to express refinement and conformance for state-transition systems with inputs and outputs, such as ioco and uioco in the area of model-based testing, and alternating simulation and alternating-trace containment originating from game theory and formal verification. Several papers have compared these independently developed relations, but these comparisons make assumptions (e.g., input-enabledness), pose restrictions (e.g., determinism - then they all coincide), use different models (e.g., interface automata and Kripke structures), or do not deal with the concept of quiescence. In this paper, we present the integration of the ioco/uioco theory of model-based testing and the theory of alternating refinements, within the domain of non-deterministic, non-input-enabled interface automata. A standing conjecture is that ioco and alternating-trace containment coincide. Our main result is that this conjecture does not hold, but that uioco coincides with a variant of alternating-trace containment, for image finite interface automata and with explicit treatment of quiescence. From the comparison between ioco theory and alternating refinements, we conclude that ioco and the original relation of alternating-trace containment are too strong for realistic black-box scenarios. We present a refinement relation which can express both uioco and refinement in game theory, while being simpler and having a clearer observational interpretation.

cs.FL↗