Database-backed web applications are prone to performance bugs related to database
accesses. While much work has been conducted on database-access antipatterns with …

High data quality is fundamental for today's AI-based systems. However, although data
quality has been an object of research for decades, there is a clear lack of research on …

Code smells indicate software design problems that harm software quality. Data-intensive
systems that frequently access databases often suffer from SQL code smells besides the …

Dockerfile plays an important role in the Docker-based software development process, but
many Dockerfile codes are infected with smells in practice. Understanding the occurrence of …

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 …

The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife
preservation, autonomous driving and criminal justice system calls for a data-centric …

Designing and deploying a hybrid data persistence architecture that involves a combination
of relational and NoSQL databases is a complex, technically challenging, and error-prone …

The ever-increasing amount, variety as well as generation and processing speed of today's
data pose a variety of new challenges for develo** Data-Intensive Software Systems …

The emergence of database-as-a-service platforms has made deploying database
applications easier than before. Now, developers can quickly create scalable applications …

Trustworthy data in the Industrial Internet of Things are paramount to ensure correct strategic
decision-making and accurate actions on the shop floor. However, the enormous amount of …