Data errors are a common challenge in machine learning (ML) projects and generally cause
significant performance degradation in ML-enabled software systems. To ensure early …

Data validation is an essential requirement to ensure the reliability and quality of Machine
Learning-based Software Systems. However, an exhaustive validation of all data fed to …

This paper demonstrates the systematic use of combinatorial coverage for selecting and
characterizing test and training sets for machine learning models. The presented work …

Recently, there has been a significant increase in the number of Artificial Intelligence (AI)
systems, software, and components. As a result, it is crucial to evaluate their quality. Quality …

Water quality indices (WQIs) are used for the simple assessment and classification of the
water quality of surface water sources. However, considerable time, financial resources, and …

Nowadays, we are witnessing an increasing demand in both corporates and academia for
exploiting Deep Learning (DL) to solve complex real-world problems. A DL program …

The development and training of deep learning models have become increasingly costly
and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for …

Automata learning is a technique to infer an automaton model of a black-box system via
queries to the system. In recent years it has found widespread use both in industry and …

Foundation models (FMs), such as Large Language Models (LLMs), have revolutionized
software development by enabling new use cases and business models. We refer to …

Test case generation tools for REST APIs have grown in number and complexity in recent
years. However, their advanced capabilities for automated input generation contrast with the …