The rapid growth of the robotics field is driven by increasing demand for enhanced performance, characterized by high reliability and repeatability capabilities. As industries strive to maintain elevated quality performance throughout their production pipelines, the complexity of the challenges they face grows in tandem. This complexity extends to processing algorithms, needing greater computational power for faster and more effective solutions. In this context, emerging technologies such as Cloud Computing present promising opportunities to augment planning tasks in robotics. This literature review explores the intersection of robotics and Cloud Computing, investigating how leveraging cloud resources can enhance planning performance in response to evolving industry demands.

Mutation testing has evolved beyond academic research, is deployed in industrial and open-source settings, and is increasingly part of universities' software engineering curricula. While many mutation testing tools exist, each with different strengths and weaknesses, integrating them into educational activities and exercises remains challenging due to the tools' complexity and the need to integrate them into a development environment. Additionally, it may be desirable to use different tools so that students can explore differences, e.g., in the types or numbers of generated mutants. Asking students to install and learn multiple tools would only compound technical complexity and likely result in unwanted differences in how and what students learn. This paper presents FRAFOL, a framework for learning mutation testing. FRAFOL provides a common environment for using different mutation testing tools in an educational setting.