In this literature, we present an empirical study of around 5K SO posts (questions + accepted answers) that contain discussions of nine popular LCSD platforms. We find that most of the questions are related to the development phase, and low-code developers also face challenges with automated testing. Our study findings offer implications for low-code practitioners, platform providers, educators, and researchers.
In this literature, we conduct an in-depth literature review of a large volume of research papers that focused on the quality assurance of ML models. We developed a taxonomy of MLSA quality assurance issues by mapping the various ML adoption challenges across different phases of SDLC. We provide recommendations and research opportunities to improve SDLC practices based on the taxonomy. This mapping can help prioritize quality assurance efforts of MLSAs where the adoption of ML models can be considered crucial.
In this literature, we have devised a novel approach for reducing power consumption of digital signage as well as satisfying human visibility by exploiting duty cycle.