Jácome Costa Cunha

Abstract
In the last few years we have been seeing a drastic change in the way software is developed. Large-scale software projects are being assembled by a flexible composition of many (small) components possibly written in different programming languages and deployed anywhere in the cloud - the so-called microservices-based applications. The dramatic growth in popularity of microservices-based applications has pushed several companies to apply major refactorings to their software systems. However, this is a challenging task that may take several months or even years. We propose a methodology to automatically evolve monolithic web applications that use object-relational mapping into microservices-based ones. Our methodology receives the source code and a microservices proposal and refactors the original code to create each microservice. Our methodology creates an API for each method call to classes that are in other services. The database entities are also refactored to be included in the corresponding service. The evaluation performed in 120 applications shows that our tool can successfully refactor about 72% of them. The execution of the unit tests in both versions of the applications yield exactly the same results.

Abstract
Data collection is pervasively bound to our digital lifestyle. A recent study reports that the growth of the data created and replicated in 2020 was even higher than in the previous years to an astonishing global amount of 64.2 zettabytes of data. There are numerous companies whose services/products rely heavily on data analysis, and mining the produced data has already revealed great value for businesses in different sectors. In order to be able to support the professionals that do this job, typically known as data scientists, we first need to characterize them. To contribute towards this characterization, we conducted a public survey and in this work we present the results about a particular aspects of their life: the tools they use and need. © 2022 IEEE Computer Society. All rights reserved.

Abstract

Abstract
This paper compares a large set of programming languages regarding their efficiency, including from an energetic point-of-view. Indeed, we seek to establish and analyze different rankings for programming languages based on their energy efficiency. The goal of being able to rank programming languages based on their energy efficiency is both recent, and certainly deserves further studies. We have taken rigorous and strict solutions to 10 well defined programming problems, expressed in (up to) 27 programming languages, from the well known Computer Language Benchmark Game repository. This repository aims to compare programming languages based on a strict set of implementation rules and configurations for each benchmarking problem. We have also built a framework to automatically, and systematically, run, measure and compare the energy, time, and memory efficiency of such solutions. Ultimately, it is based on such comparisons that we propose a series of efficiency rankings, based on single and multiple criteria. Our results show interesting findings, such as how slower/faster languages can consume less/more energy, and how memory usage influences energy consumption. We also present a simple way to use our results to provide software engineers and practitioners support in deciding which language to use when energy efficiency is a concern. In addition, we further validate our results and rankings against implementations from a chrestomathy program repository, Rosetta Code., by reproducing our methodology and benchmarking system. This allows us to understand how the results and conclusions from our rigorously and well defined benchmarked programs compare to those based on more representative and real-world implementations. Indeed our results show that the rankings do not change apart from one programming language.

Abstract
Microservices emerged as one of the most popular architectural patterns in the recent years given the increased need to scale, grow and flexibilize software projects accompanied by the growth in cloud computing and DevOps. Many software applications are being submitted to a process of migration from its monolithic architecture to a more modular, scalable and flexible architecture of microservices. This process is slow and, depending on the project's complexity, it may take months or even years to complete. This paper proposes a new approach on microservice identification by resorting to topic modelling in order to identify services according to domain terms. This approach in combination with clustering techniques produces a set of services based on the original software. The proposed methodology is implemented as an open-source tool for exploration of monolithic architectures and identification of microservices. A quantitative analysis using the state of the art metrics on independence of functionality and modularity of services was conducted on 200 open-source projects collected from GitHub. Cohesion at message and domain level metrics' showed medians of roughly 0.6. Interfaces per service exhibited a median of 1.5 with a compact interquartile range. Structural and conceptual modularity revealed medians of 0.2 and 0.4 respectively. Our first results are positive demonstrating beneficial identification of services due to overall metrics' results. © 2021 ACM.