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The accelerating adoption of large language models, retrieval-augmented generation pipelines, and multi-agent AI workflows has created a structural governance crisis. Organizations cannot govern what they cannot see, and existing compliance…

Excessive reliance on validation performance during model selection can lead to validation overfitting (VO), where models appear effective during development but fail at test time. This issue is further amplified in low-data regimes and…

Database research and the development of learned query optimisers rely heavily on realistic SQL workloads. Acquiring real-world queries is increasingly difficult, however, due to strict privacy regulations, and publicly released anonymised…

OpenMP is the de facto standard to exploit the on-node parallelism in new generation supercomputers.Despite its overall ease of use, even expert users are known to create OpenMP programs that harbor concurrency errors, of which one of the…

We present a prototype that implements a set of logical rules to prove the satisfiability for a class of specifications on XML documents. Specifications are given by means of constrains built on Boolean XPath patterns. The main goal of this…

We present a randomized differential testing approach to test OpenMP implementations. In contrast to previous work that manually creates dozens of verification and validation tests, our approach is able to randomly generate thousands of…

Recurrent neural networks (RNNs) have been widely applied to various sequential tasks such as text processing, video recognition, and molecular property prediction. We introduce the first coverage-guided testing tool, coined testRNN, for…

We present SilVer (Silq Verification), an automated tool for verifying behaviors of quantum programs written in Silq, which is a high-level programming language for quantum computing. The goal of the verification is to ensure correctness of…

Software engineers develop, fine-tune, and deploy deep learning (DL) models using a variety of development frameworks and runtime environments. DL model converters move models between frameworks and to runtime environments. Conversion…

Automated and therefore repeatable tests are important in product lines to ensure and maintain quality of software functions as well as efficiency of the developers. This article shows methods for fully automated testing of SIMULINK models…

adaptNMT streamlines all processes involved in the development and deployment of RNN and Transformer neural translation models. As an open-source application, it is designed for both technical and non-technical users who work in the field…

OpenACC is a high-level directive-based parallel programming model that can manage the sophistication of heterogeneity in architectures and abstract it from the users. The portability of the model across CPUs and accelerators has gained the…

In this paper, we present Coyote C++, a fully automated white-box unit testing tool for C and C++. Whereas existing tools have struggled to realize unit test generation for C++, Coyote C++ is able to produce high coverage results from unit…

There is an increase in deploying Deep Learning (DL)-based software systems in real-world applications. Usually DL models are developed and trained using DL frameworks that have their own internal mechanisms/formats to represent and train…

A syntactic model is presented for the specification of finite-state synchronous digital logic systems with complex input/output interfaces, which control the flow of data between opaque computational elements, and for the composition of…

In ontology-mediated query answering, access to incomplete data sources is mediated by a conceptual layer constituted by an ontology. To correctly compute answers to queries, it is necessary to perform complex reasoning over the constraints…

Today, tool-calling agents are commonly evaluated or tested on static datasets of execution traces, including input commands, agent responses, and associated tool calls. However, internal production datasets are often insufficient or…

Service-based systems are software systems composed of autonomous components or services provided by different vendors, deployed on remote machines and accessible through the web. One of the challenges of modern software engineering is to…

Modern intrusion detection systems (IDS) leverage graph neural networks (GNNs) to detect malicious activity in system provenance data, but their decisions often remain a black box to analysts. This paper presents a comprehensive XAI…

Operational Neural Networks (ONNs) have recently been proposed as a special class of artificial neural networks for grid structured data. They enable heterogenous non-linear operations to generalize the widely adopted convolution-based…