Abstract
The architectural style of microservices has received much attention from both business and academia and converting a monolithic application into a microservice-based one has become a regular practice. However, companies struggle with migrating their existing monolithic applications to microservices and software engineers frequently face challenges due to a lack of awareness of alternative migration methodologies, making the migration process even harder. In this paper, we present a framework to help software engineers during the migration process by addressing gaps in understanding various migration tools and approaches, allowing for easy comparison between multiple options. Our tool combines multiple existing approaches into one platform, allowing a comprehensive visualization of migration proposals and comparing different options offered by already existing approaches.

Abstract
Continuous Integration and Continuous Deployment (CI/CD) pipelines play a vital role in the DevOps process, enabling developers to automate and enhance software delivery. However, the existence of multiple technologies, such as GitHub Actions, GitLab CI/CD, or Jenkins, poses challenges due to their lack of interoperability and the use of different programming languages for pipeline construction. To address these challenges and improve the CI/CD process, our objective is to develop a block-based language specifically designed for representing CI/CD pipelines. With our language, we intend to empower users to more easily create correct pipelines. Through an interactive and user-friendly process, our approach guides users in constructing pipelines, ensuring accuracy and reducing errors. Additionally, our language will facilitate seamless transitions between different pipeline technologies, providing users with flexibility and ease of adoption.

Abstract
In recent years, the research community has raised serious questions about the replicability and reproducibility of scientific work. In particular, since many studies include some kind of computing work, these are also technological challenges, not only in computer science but in most research domains. Replicability and reproducibility are not easy to achieve, not only because researchers have diverse proficiency in computing technologies, but also because of the variety of computational environments that can be used. Indeed, it is challenging to recreate the same environment using the same frameworks, code, programming languages, dependencies, and so on. In this work, we propose a vision for an Integrated Development Environment allowing the creation, configuration, execution, packaging, and sharing of scientific computational experiments. Such a framework should allow researchers to easily set the code and data used and define the programming languages, code, dependencies, databases, or commands to execute to achieve consistent results for each experiment. With this work, we intend to aid researchers by integrating into the same platform all the stages of the design, execution, and analysis of a computational experiment.

Abstract
Although remote work was already possible and used in some contexts, the COVID-19 pandemic made it normal and, in some situations, even mandatory. This was the case in Portugal and in particular in its software industry. Given this abrupt change in how we work, it became pressing to investigate the impacts of this profound change to remote work, so that we can cope with the potential negative consequences (professional, personal, etc.). Thus, the goal of this work is to study the impact of the referred change to remote work, due to the COVID-19 pandemic, on software professionals in Portugal. To achieve this goal, a survey was prepared and distributed via email, LinkedIn, and Instagram. In total, 176 valid answers were collected from software professionals working in Portugal from 38 different companies. After the performed statistical analysis on the targeted population and focusing on the 10 elaborated research questions, two major findings can be concluded with certainty: (i) having worked in a remote regime before the pandemic period has a strong relationship with a higher frequency of use of teleconference tools after this period, and (ii) participants who do not feel safe about coming back to a fully on-site regime are more likely to prefer a fully remote regime than the ones who feel safe, while the latter group is more likely to prefer a hybrid regime. © 2023 CIbSE 2023 - XXVI Ibero-American Conference on Software Engineering. All rights reserved.

Abstract
In recent years, the research community, but also the general public, has raised serious questions about the reproducibility and replicability of scientific work. Since many studies include some kind of computational work, these issues are also a technological challenge, not only in computer science, but also in most research domains. Computational replicability and reproducibility are not easy to achieve due to the variety of computational environments that can be used. Indeed, it is challenging to recreate the same environment via the same frameworks, code, programming languages, dependencies, and so on. We propose a framework, known as SciRep, that supports the configuration, execution, and packaging of computational experiments by defining their code, data, programming languages, dependencies, databases, and commands to be executed. After the initial configuration, the experiments can be executed any number of times, always producing exactly the same results. Our approach allows the creation of a reproducibility package for experiments from multiple scientific fields, from medicine to computer science, which can be re-executed on any computer. The produced package acts as a capsule, holding absolutely everything necessary to re-execute the experiment. To evaluate our framework, we compare it with three state-of-the-art tools and use it to reproduce 18 experiments extracted from published scientific articles. With our approach, we were able to execute 16 (89%) of those experiments, while the others reached only 61%, thus showing that our approach is effective. Moreover, all the experiments that were executed produced the results presented in the original publication. Thus, SciRep was able to reproduce 100% of the experiments it could run. © 2025 The Authors

Abstract
Algebraic theories for modeling components and their interactions offer abstraction over the specifics of component states and interfaces. For example, such theories deal with forms of sequential composition of two components in a manner independent of the type of data stored in the states of the components, and independent of the number and types of methods offered by the interfaces of the combinators. General purpose programming languages do not offer this level of abstraction, which implies that a gap must be bridged when turning component models into implementations. In this paper, we present an approach to prototyping of component-based systems that employs so-called type-level programming (or compile-time computation) to bridge the gap between abstract component models and their type-safe implementation in a functional programming language. We demonstrate our approach using Barbosa's model of components as generalized Mealy machines. For this model, we develop a combinator library in Haskell, which uses type-level programming with two effects. Firstly, wiring between components is computed during compilation. Secondly, the well-formedness of the component compositions is guarded by Haskell's strong type system. Copyright 2007 ACM.