There is a widespread demand for Artificial Intelligence (AI) software, specifically Machine
Learning (ML). It is getting increasingly popular and being adopted in various applications …

Low-code software development (LCSD) is an emerging approach to democratize traditional
and Machine Learning (ML) application development for practitioners from diverse …

Machine Learning (ML) projects incur novel challenges in their development and
productionisation over traditional software applications, though established principles and …

Sentiment detection in software engineering (SE) has shown promise to support diverse
development activities. However, given the diversity of SE platforms, SE-specific sentiment …

As big data grows ubiquitous across many domains, more and more stakeholders seek to
develop Machine Learning (ML) applications on their data. The success of an ML …

Abstract The traditional software space (1.0) has seen more than fifty years of creation,
testing, and delivery of deterministic software, but this tradition is being disrupted by …

When dealing with real business problems at a company, developing an artificial
intelligence system no longer only relies on the success of compiling a program. The …

Context: The proficiency of machine learning (ML) systems in solving many real-world
problems effectively has enabled a paradigm shift toward ML-enabled systems. In ML …

Fairness, ie, lack of bias during a decision-making process is a desirable property in any
software system that is used to make critical decisions (eg, mortgage approval). However …

Extending the work of Downing & Badar (2022), this paper presents the history of software,
artificial intelligence, software quality assurance, and a software QA architecture called the …