Related papers: From Temporal Models to Property-Based Testing
Using probabilities in the formal-methods-based development of safety-critical software has quickened interests in academia and industry. We address this area by our model-driven engineering method for reactive systems SPACE and its…
Hyperproperties extend trace properties to express properties of sets of traces, and they are increasingly popular in specifying various security and performance-related properties in domains such as cyber-physical systems, smart grids, and…
Behavior Trees (BT) are becoming quite popular as an Acting component of autonomous robotic systems. We propose to define a formal semantics to BT by translating them to a formal language which enables us to perform verification of programs…
In the context of a large software testing company, we have deployed the model-based testing (MBT) approach to take the company's test automation practices to higher levels of maturity /and capability. We have chosen, from a set of…
Large language models are becoming increasingly practical for translating code across programming languages, a process known as $transpiling$. Even though automated transpilation significantly boosts developer productivity, a key concern is…
The increasing use of model-based tools enables further use of formal verification techniques in the context of distributed real-time systems. To avoid state explosion, it is necessary to construct verification models that focus on the…
This paper is about modeling and verification languages with their pros and cons. Modeling is dynamic part of system development process before realization. The cost and risky situations obligate designer to model system before production…
Model-based testing (MBT) is a well-known technology, which allows for automatic test case generation, execution and evaluation. To test non-functional properties, a number of test MBT frameworks have been developed to test systems with…
Signal Temporal Logic (STL) is an expressive formal language for specifying spatio-temporal requirements over real-valued, real-time signals. It has been widely used for the verification and synthesis of autonomous systems and…
Spatial aspects of computation are becoming increasingly relevant in Computer Science, especially in the field of collective adaptive systems and when dealing with systems distributed in physical space. Traditional formal verification…
Recent research has explored how Language Models (LMs) can be used for feature representation and prediction in tabular machine learning tasks. This involves employing text serialization and supervised fine-tuning (SFT) techniques. Despite…
Delta modeling is a modular, yet flexible approach to capture spatial and temporal variability by explicitly representing the differences between system variants or versions. The conceptual idea of delta modeling is language-independent.…
The static ``train then deploy" paradigm fundamentally limits Large Language Models (LLMs) from dynamically adapting their weights in response to continuous streams of new information inherent in real-world tasks. Test-Time Training (TTT)…
We present a model-based testing approach to support automated test generation with domain-specific concepts. This includes a language expert who is an expert at building test models and domain experts who are experts in the domain of the…
The test is one of the approaches commonly used for validating systems to ensure qualitative and quantitative implementation requirements. In this paper, we interest in formal testing using graph transformation, thus we propose an approach…
Various formal languages have been proposed in the literature for the individual-based modelling of ecological systems. These languages differ in their treatment of time and space. Each modelling language offers a distinct view and…
Learning dynamical systems properties from data provides important insights that help us understand such systems and mitigate undesired outcomes. In this work, we propose a framework for learning spatio-temporal (ST) properties as formal…
The large transformer-based language models demonstrate excellent performance in natural language processing. By considering the transferability of the knowledge gained by these models in one domain to other related domains, and the…
We study the generalization abilities of language models when translating natural language into formal specifications with complex semantics. In particular, we fine-tune language models on three datasets consisting of English sentences and…
\textit{Background:} The use of large language models in software testing is growing fast as they support numerous tasks, from test case generation to automation, and documentation. However, their adoption often relies on informal…