Large Language Models (LLMs) are starting to be profiled as one of the most significant disruptions in the Software Testing field. Specifically, they have been successfully applied in software testing tasks such as generating test code, or summarizing documentation. This potential has attracted hundreds of researchers, resulting in dozens of new contributions every month, hardening researchers to stay at the forefront of the wave. Still, to the best of our knowledge, no prior work has provided a structured vision of the progress and most relevant research trends in LLM-based testing. In this article, we aim to provide a roadmap that illustrates its current state, grouping the contributions into different categories, and also sketching the most promising and active research directions for the field. To achieve this objective, we have conducted a semi-systematic literature review, collecting articles and mapping them into the most prominent categories, reviewing the current and ongoing status, and analyzing the open challenges of LLM-based software testing. Lastly, we have outlined several expected long-term impacts of LLMs over the whole software testing field.

Abstract--We present a local-to-global and measure-theoretical approach to understanding datasets. The core idea is to form ulate a logifold structure and to interpret network models with restricted domains as local charts of datasets. In particul ar, this provides a mathematical foundation for ensemble machi ne learning. Our experiments demonstrate that logifolds can b e implemented to identify fuzzy domains and improve accuracy compared to taking average of model outputs. Additionally, we provide a theoretical example of a logifold, highlighting t he importance of restricting to domains of classifiers in an ens emble.

When data on building features is unavailable, the task of determining how to improve that building in terms of carbon emissions becomes infeasible. We show that from only a set of images, a Large Language Model with appropriate prompt engineering and domain knowledge can successfully estimate a range of building features relevant for sustainability calculations. We compare our novel image-to-data method with a ground truth comprising real building data for 47 apartments and achieve accuracy better than a human performing the same task. We also demonstrate that the method can generate tailored recommendations to the owner on how best to improve their properties and discuss methods to scale the approach.

The World Customs Organization (WCO) recently conducted a BACUDA Data Analytics workshop for the Maldives Customs Service with 41 participants from the 30th of January to the 1st of February in Male, Maldives. The mission was financed by the Customs Cooperation Fund of Korea (CCF-Korea) and took place under the WCO's BACUDA initiative, the WCO capacity building project on Data Analytics. WCO experts and two BACUDA Scholarship graduates led the workshop. They delivered various sessions to equip the customs officials with the latest data analytics tools and techniques. One of the key highlights of the workshop was a hands-on session where the participants learned how to use Python language to work with the AI HS algorithm developed through the BACUDA project.

Prime Minister Narendra Modi has said India is facing a "long battle" ahead in its efforts to defeat the pandemic as the country set a new record for daily coronavirus infections. United States President Donald Trump has said the US is "terminating" its relationship with the World Health Organization (WHO), saying the agency has not made coronavirus reforms. The WHO and 37 countries launched the COVID-19 Technology Access Pool, an alliance aimed at making coronavirus vaccines, tests, treatments and other technologies available to all countries. More than 5.9 million cases of coronavirus have been confirmed around the world, according to data from Johns Hopkins University. Some 365,000 people have died, while more than 2.4 million have recovered.