Rural credit is one of the most critical inputs for farm production across the globe. Despite so
many advances in digitalization in emerging and developing economies, still a large part of …

Context: Artificial intelligence (AI) methods and models have extensively been applied to
support different phases of the software development lifecycle, including software testing …

The software of systems of elevators needs constant maintenance to deal with new
functionality, bug fixes, or legislation changes. To automatically validate the software of …

Unit testing is a stage of testing where the smallest segment of code that can be tested in
isolation from the rest of the system—often a class—is tested. Unit tests are typically written …

Background: The field of software testing is growing and rapidly-evolving. Aims: Based on
keywords assigned to publications, we seek to identify predominant research topics and …

When deploying Deep Neural Networks (DNNs), developers often convert models from one
deep learning framework to another (eg, TensorFlow to PyTorch). However, this process is …

Context: Recommender systems are widely used in various fields because they can provide
decision-making guidance to users facing an overwhelming set of choices. In previous …

Metamorphic testing addresses the issue of the oracle problem by comparing results
transformation from multiple test executions. The relationship that governs the output …

Converting deep learning models between frameworks is a common step to maximize
model compatibility across devices and leverage optimization features that may be …

Abstract Machine learning (ML) may enable effective automated test generation. We
characterize emerging research, examining testing practices, researcher goals, ML …