Abstract
The increasing use of microservices architectures has been accompanied by the profusion of tools for their design and operation. One relevant tool is API Gateways, which work as a proxy for microservices, hiding their internal APIs, providing load balancing, and multiple encoding support. Particularly in cloud environments, where the inherent flexibility allows on-demand resource deployment, API Gateways play a key role in seeking quality of service. Although multiple solutions are currently available, a comparative performance assessment under real workloads to support selecting the more suitable one for a specific service is time-consuming. In this way, the present work introduces AGE, a service capable of automatically deploying multiple API Gateways scenarios and providing a simple comparative performance indicator for a defined workload and infrastructure. The designed proof of concept shows that AGE can speed up API Gateway deployment and testing in multiple environments. © 2023 IEEE.

Abstract

Abstract
Type systems and type inference systems can be used to help text and code generation models like GPT-3 produce more accurate and appropriate results. These systems provide information about the types of variables, functions, and other elements in a program or codebase, which can be used to guide the generation of new code or text. For example, a code generation model that is aware of the types of variables and functions being used in a program can generate code that is more likely to be syntactically correct and semantically meaningful. We argue for the specialization of language models such as GPT-3 for automatic program repair tasks, incorporating type information in the model's learning process. A trained language model is expected to perform better by understanding the nuances of type systems and using them for program repair, instead of just relying on the general structure of programs.

Abstract
We present an imperative refinement language for the development of backtracking programs and discuss its semantic foundations. For expressivity, our language includes prospective values and preference - the latter being a variant of Nelson's biased choice that backtracks from infeasibility of a continuation. Our key contribution is to examine feasibility-preserving refinement as a basis for developing backtracking programs, and several key refinement laws that enable compositional refinement in the presence of non -monotonic program combinators.

Abstract
This paper presents GLITCH, a new technology-agnostic framework that enables automated polyglot code smell detection for Infrastructure as Code scripts. GLITCH uses an intermediate representation on which different code smell detectors can be defined. It currently supports the detection of nine security smells and nine design & implementation smells in scripts written in Ansible, Chef, Docker, Puppet, or Terraform. Studies conducted with GLITCH not only show that GLITCH can reduce the effort of writing code smell analyses for multiple IaC technologies, but also that it has higher precision and recall than current state-of-the-art tools. A video describing and demonstrating GLITCH is available at: https://youtu.be/E4RhCcZjWbk.

Abstract
Cities worldwide have agreed on ambitious goals regarding carbon neutrality; thus, smart cities face challenges regarding active and shared mobility due to public transportation's low attractiveness and lack of real-time multimodal information. These issues have led to a lack of data on the community's mobility choices, traffic commuters' carbon footprint and corresponding low motivation to change habits. Besides, many consumers are reluctant to use some software tools due to the lack of data privacy guarantee. This paper presents a methodology developed in the FranchetAI project that addrebes these issues by providing distributed privacy-preserving machine learning models that identify travel behaviour patterns and respective GHG emissions to recommend alternative options. Also, the paper presents the developed FranchetAI mobile prototype. © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)