相关论文: Formally Checking Large Data Sets in the Railways
Development of formal proofs of correctness of programs can increase actual and perceived reliability and facilitate better understanding of program specifications and their underlying assumptions. Tools supporting such development have…
Reading comprehension is one of the crucial tasks for furthering research in natural language understanding. A lot of diverse reading comprehension datasets have recently been introduced to study various phenomena in natural language,…
\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…
Checking data quality against domain knowledge is a common activity that pervades statistical analysis from raw data to output. The R package 'validate' facilitates this task by capturing and applying expert knowledge in the form of…
The dissemination of false information on online platforms presents a serious societal challenge. While manual fact-checking remains crucial, Large Language Models (LLMs) offer promising opportunities to support fact-checkers with their…
Model checking and automated theorem proving are two pillars of formal methods. This paper investigates model checking from an automated theorem proving perspective, aiming at combining the expressiveness of automated theorem proving and…
The demonstrated code-understanding capability of LLMs raises the question of whether they can be used for automated program verification, a task that demands high-level abstract reasoning about program properties that is challenging for…
In the railway domain, an electronic interlocking is a computerised system that controls the railway signalling components (e.g. switches or signals) in order to allow a safe operation of the train traffic. Interlockings are controlled by a…
We present an application of conformal prediction, a form of uncertainty quantification with guarantees, to the detection of railway signals. State-of-the-art architectures are tested and the most promising one undergoes the process of…
Despite the vast body of research literature proposing algorithms with formal guarantees, the amount of verifiable code in today's systems remains minimal. This discrepancy stems from the inherent difficulty of verifying code, particularly…
AutomationML (AML) enables standardized data exchange in engineering, yet existing recommendations for proper AML modeling are typically formulated as informal and textual constraints. These constraints cannot be validated automatically…
Traffic prediction is pivotal for rational transportation supply scheduling and allocation. Existing researches into short-term traffic prediction, however, face challenges in adequately addressing exceptional circumstances and integrating…
The increasing complexity of modern interlocking poses a major challenge to ensuring railway safety. This calls for application of formal methods forassurance and verification of their safety. We have developed an industry-strength toolset,…
Large Language Models (LLM) are evolving and have significantly revolutionized the landscape of software development. If used well, they can significantly accelerate the software development cycle. At the same time, the community is very…
The two major systems of formal verification are model checking and algebraic model-based testing. Model checking is based on some form of temporal logic such as linear temporal logic (LTL) or computation tree logic (CTL). One powerful and…
Event-B is one of more popular notations for model-based, proof driven specification. It offers a fairly high-level mathematical lan- guage based on FOL and ZF set theory and an economical yet expres- sive modelling notation. Model…
System modeling is a classical approach to ensure their reliability since it is suitable both for a formal verification and for software testing techniques. In the context of model-based testing an approach combining random testing and…
In the current machine learning landscape, we face a "model lake" phenomenon: Given a task, there is a proliferation of trained models with similar performances despite different behavior. For model users attempting to navigate and select…
Formal specification languages have long languished, due to the grave scalability problems faced by complete verification methods. Runtime verification promises to use formal specifications to automate part of the more scalable art of…
To take advantage of Large Language Model in theorem formalization and proof, we propose a reinforcement learning framework to iteratively optimize the pretrained LLM by rolling out next tactics and comparing them with the expected ones.…