Incorporating machine learning (ML) components into software products raises new
software-engineering challenges and exacerbates existing ones. Many researchers have …

Background: As Machine Learning (ML) advances rapidly in many fields, it is being adopted
by academics and businesses alike. However, ML has a number of different challenges in …

The nondeterminism of Deep Learning (DL) training algorithms and its influence on the
explainability of neural network (NN) models are investigated in this work with the help of …

Abstract Machine learning (ML) is extensively used in production-ready applications, calling
for mature engineering techniques to ensure robust development, deployment and …

Despite the increasing interest in quantum computing, the aspect of development to achieve
cost-effective and reliable quantum software applications has been slow. One barrier is the …

Context: The abundance of software artifacts in open-source repositories has been analyzed
by researchers from many perspectives, to address challenges in downstream tasks such as …

Accurate electricity forecasting is essential for smart cities to maintain grid stability by
allocating resources in advance, ensuring better integration with renewable energies, and …

Context: Machine learning has proved an efficient tool, but the systems need tools to
mitigate risks during runtime. One approach is fault tolerance: detecting and handling errors …

The increasing integration of machine learning (ML) in modern software systems has lead to
new challenges as a result of the shift from human-determined behavior to data-determined …

The traditional approach of categorizing test results as “Pass” or “Fail” based on fixed
thresholds can be labor-intensive and lead to dropping test data. This paper presents a …