Abstract Finding software developers with expertise in specific technologies that align with industry domains is an increasingly critical requirement. However, due to the ever‐changing nature of the technology industry, locating these professionals has become a significant challenge for companies and institutions. This research presents a comprehensive overview of studies exploring suitable recommendation systems that can assist companies in addressing this pressing need. To conduct this study, we employ a hybrid systematic mapping approach with an initial number of 1,251 studies and a final selection of 21 studies. Our work focuses on collecting data on key technologies, methodologies, and data sets utilized in proposed recommendation systems, to design a new recommendation system that can effectively identify specialists capable of aligning specific technical knowledge with industry domains. The outcomes of this study include insights into the current research trends in this field, alongside a practical overview of considerations necessary for developing a recommendation system that successfully meets the criteria for aligning technical skills with industry domains. By following a hybrid systematic mapping methodology and presenting the outcomes in the form of insights, this research addresses the challenge of finding software developers with domain‐specific expertise in a rapidly changing technology industry, laying the groundwork for aligning technical skills with industry domains.

Finding software development experts who address the specific needs of companies is essential in software development environments. However, companies have struggled to find experts who master specific technical skills aligned with the experience of acting in specific industry domains. To address this issue, we propose a recommendation system approach that helps to identify and classify eligible experts. In order to achieve this goal, data from repository databases are extracted, and a complex network is modeled considering a combination of three main aspects: i) the impact of a developer on the repositories of the industry context, ii) the technologies considered in the search terms, and iii) the date when the developer contributed to the project. A final recommendation experts list is presented in three contexts considering different technologies and industry domains, followed by a collaboration trends discussion.

Development communities and the software industry increasingly adopt the Software Ecosystems approach (SECO). This approach can provide advantages but add additional complexity to resource management, affecting the software supply network. Observing SECOs, we can see through three dimensions: business, technical, and social. The social dimension focuses on stakeholders and how they interact with other dimensions. This paper presents a process for analyzing the social dimension of Software Ecosystems, supported by Complex Networks metrics, which allow the presentation of existing SECO relationships’ through visualizations and the use of complex networks. A preliminary evaluation with real data was carried out. The results point to the solution’s viability.

In the Internet of Things (IoT) era, technologies have become even more immersive and ubiquitous in people’s lives. Therefore, developing collaborative systems becomes a more complex task involving different factors and actors. The nature of these applications encompasses different stakeholders and interests. The software development process encompasses large-scale sensors and actuators to characterize appropriated the Internet of Things (IoT). The introduction of this technology in the scenario of collaborative systems and IoT increased the complexity of software development. This work presents a computational architecture based on middleware to deal with the challenges of developing collaborative systems in the context of IoT. As a contribution, results acquired from the case study demonstrate how the architecture supports IoT application development in the context of collaborative systems.

Knowledge Base Question Answering systems (KBQA) aim to find answers to natural language questions over a knowledge base. This work presents a template matching approach for Complex KBQA systems (C-KBQA) using the combination of Semantic Parsing and Neural Networks techniques to classify natural language questions into answer templates. An attention mechanism was created to assist a Tree-LSTM in selecting the most important information. The approach was evaluated on the LC-Quad 1, LC-Quad 2, ComplexWebQuestion, and WebQuestionsSP datasets, and the results show that our approach outperforms other approaches on three datasets.

Finding experts that can help address critical elements or problems in a project is a challenging task. This is especially true in global software development where there is often a need to identify developers with specific skill sets and expertise. It is also essential to identify developers that can help move the project forward. To address this, we propose an expert recommendation system to help identify and classify individuals with the expertise and skills that could collaborate on a project. To achieve this goal, we model a popular project on GitHub as a contribution network. Our approach analyzes both syntactic and semantic aspects of the data by exploring the network with machine learning algorithms and considering an ontology that can be used to extract topics from the project's key terms. Our approach looks to recommend individuals with the necessary skills, have been strong contributors in the past, and have a positive interest trend.

Finding developers to assist with project issues is essential in Global Software Development (GSD) contexts, where various individuals with distinct characteristics are involved. Several recommending approaches lead to identifying the same group of individuals who end up work overloaded. Aiming to diversify the recommendation process, we introduced DRecSys, a diversity-based recommendation system. Our approach seeks to identify individuals with characteristics similar to those previously requested to collaborate. To that end, we proposed a hybrid process composed of supervised (classification) and unsupervised (clustering) techniques. We provided evidence that DRecSys is able to recommend suitable non-obvious developers to assist with project issues.

The Internet of Things (IoT) is a technological paradigm that aims to connect millions of networked devices to provide more complex functionality. However, the heterogeneity of application/device communication standards precludes support for interoperability, which impacts developer collaboration. There are many works that propose solutions to support syntactic and semantic interoperability in IoT context. This paper aims to propose a service capable of supporting pragmatic IoT interoperability with the goal of enriching developer collaboration. This being possible through the use of inferences and similarity calculations about information provided by the developers.

The analysis of social structures is important in many contexts, especially in Global Software Development, where various developers with diverse skills and knowledge are involved. In this sense, searching for essential members is a valuable task since they are fundamental to the network's evolution. With the main goal of identifying and monitoring these individuals, we propose a temporal analysis approach that explores syntactic and semantic network aspects. We describe experiments based on popular projects on Github that consider a network modeled over consecutive time periods. We also propose an ontology to represent the domain knowledge and explore the network by investigating its semantic context. We provide evidence that our approach can detect individuals considered essential based on their role in the network.

An aging population has motivated research into Ambient Assisted Living (AAL) with the aim of supporting people to continue to live in their homes as they age or with chronic health problems. As part of this work, some researchers have focused on identifying and reporting daily activities of individuals at home in order to try to reduce the workload on caregivers and health professionals. Such an environment is usually monitored by non-wearable sensors that collect a vast amount of data. The use of this data requires computational methods that can process it in a reasonable time. This paper proposes an event-driven framework to detect unusual patterns in AAL environments. A Fog-Cloud paradigm and Lambda architecture are adopted as the framework to support computational solutions to deal with the volume of data, and machine learning techniques are used for recognition of daily activities. The framework was evaluated through a case study based on data collected in a real environment. Results point to the feasibility of the proposal.

Improvements in I/O architectures are becoming increasingly required nowadays. This is an essential point to complex and data intensive scalable applications. Data-Intensive Scalable Computing (DISC) and High-Performance Computing (HPC) applications frequently need to transfer data between storage resources. In the scientific and industrial fields, the storage component is a key element, because usually those applications employ a huge amount of data. Therefore, the performance of these applications commonly depends on some factors related to time spent in execution of the I/O operations. However, researchers, through their works, are proposing different approaches targeting improvements on the storage layer, thus, reducing the gap between processing and storage. Some solutions combine different hardware technologies to achieve high performance, while others develop solutions on the software layer. This paper aims to present a characterization model for classifying research works on I/O performance improvements for large scale computing facilities. Analysis over 36 different scenarios using a synthetic I/O benchmark demonstrates how the latency parameter behaves when performing different I/O operations using distinct storage technologies and approaches.

With increasingly complex activities, scientific workflows are becoming more data-intensive. In this context, may require a collaborative, distributed or high performance (HPC) environment such as grids or clouds for their execution. Considering its extensibility feature, resources pool and pay-to-use, cloud computing environments have been increasingly adopted. Scientists are formulating their scientific experiments in a collaborative way, provisioning resources (software, hardware) and managing large volumes of data, based on cloud infrastructures. In data-driven collaborative scientific experiments, aspects such interoperability, privacy and trust in shared provenance data should be considered to allow the reproducibility of the results. In this paper, we present the BlockFlow architecture, which aims to bring trust to scientists of a scientific ecosystem platform (E-SECO) in the execution of their collaborative scientific experiments on cloud platforms.

Dealing with complex networks is often a challenge due to the high computational cost in analyzing a huge amount of data. Partitioning methods can decrease the complexity of large structures by reducing them to smaller, less connected parts. Also, the data splitting allows the use of multiprocessing to accelerate the execution of data procedures with simultaneity and parallelism. In this paper, we propose a new parallel partitioning algorithm with a focus on assisting in community detection in social networks. The algorithm uses a subtree-splitting strategy, as well as boundaries defined, in order to cut the network into n balanced subnetworks. Our proposal stands out for the focus on aiding density-based approaches, such as the NetSCAN clustering algorithm, considering two particulars: (i) keeping the partitions connectivity, and; (ii) allowing node overlapping between partitions. Experiments were carried out with different instances intending to investigate the partitions obtained and evaluate our proposal. Furthermore, the algorithm performance analysis in a large network is employed, sequential and parallel implementations are compared in terms of execution time and memory consumption. Evidence was provided that the proposed algorithm is able to split an extensive data set into balanced partitions with optimistic performance results.

Digital transformation in e-health is a well-known challenge problem reported from several studies and from several dimensions. In addition, it has been verified a gap in the utilization of new technologies as differential tool in the war against the Covid-19 pandemic. In this paper, we present an ongoing research effort which is characterized for supporting a digital transformation gap found in a public primary healthcare system. Therefore, it can be seen as an interesting case study approach to tackle some challenges found in Covid-19. Utilizing smart bands by groups of different type of voluntaries, where vital signals were collected in a digital data fashion and then evaluated in public health unit. A recommendation system (RS) algorithm was also developed to understand userś behaviors, based upon their vital signals. In addition, we utilized a simulator software to highlight people movement and predictable scenarios of Covid-19 contamination. This last effort provides a visualization on how the proposal could also help in a real ordinary monitoring scenario. Initial results from this research work indicates a differentiated approach to tackle challenges in digital transformation in a public health scenario, especially in a pandemic. In addition, our experiments illustrate that the adoption of some computational technologies require mainly changes on the present behavior, from governments and people, to be successful approaches to individual protection inside public environments.

[Motivation] The science has evolved giving space to the open and data-intensive science. In this context, information sharing, and data integration are essential issues. [Problem] Data integration in the e-Science domain is a complex task, as researchers use different scientific software tools, and perform their experiments in different sites and contexts, thus using specific data models. Without supporting the execution of different geographically distributed tools, scientists may not collaborate with the experiment. [Objective] The goal of this article is to enhance collaboration between researchers throughout the process of scientific experimentation in a scientific software ecosystem platform. [Method] To achieve this goal, the article addresses an approach to support the integration of a scientific software ecosystem with external scientific platforms. [Results] An evaluation of the proposed solution showed that integration solutions implemented can enhance collaboration among researchers.

With advances in technologies, such as the Internet of Things (IoT), scientific research has become data-driven, based mainly on the intensive collection of raw data and analyses of heterogeneous data sets. Computational resources has allowed a somehow agile data production and analytics, which can be collaboratively assisted by teams of researchers. However, in order to achieve a full collaboration, with members altogether engaged, it is necessary to ensure a continuous data flow between the parts and to validate the compatibility of their data. To accomplish such aim, it is required to stress some aspects of the collaboration model, composed by processes of communication, cooperation, and coordination. This paper presents a collaborative research environment built in a scientific software ecosystem platform that implements an ontology-based data integration strategy to semantically provide comprehensive data models of disparate data sources, with the aim of enhancing the scientific collaboration.

In global software development distributed teams are often challenged by global distances, such as cultural diversity and linguistic barriers. This stimulates the search for groups of people that can work together and collaborate in a positive way, even though they are working in a distributed environment. In addition, the demand for high quality software motivates the search for experienced developers who are able to solve complex tasks or to help others with their own tasks. This work aims to detect experts and to identify collaborative groups with experienced members in some topics of software development. To achieve these goals the StackOverflow forum was used and modeled as a complex network. The presented method uses NetSCAN algorithm to detect overlapping communities, expert developers and their topics of expertise in social networks. Through a temporal analysis over the social network multidisciplinary developers were found as well as people who are changing their interests over time. In a first evaluation a statistic test showed that experts (indicated by the proposed method) have a higher performance than common users. A second evaluation showed that these experts are suitable for recommendation systems in question-answer forums.

Finding experts in certain technologies or modules inside a project on global software development can be a challenge. There is a large pool of developers, with distinct knowledge, availability and goals. Furthermore, work times, dedication and cooperation expertise impact directly willingness and capacity to collaborate. To address this scenario, we propose a collaborative network to find experts within projects that can be helpful to community members. We adopt a clustering algorithm in order to find experts not only with specific knowledge, but also expertise in collaboration on the topics required. We choose a popular open source project from GitHub to evaluate our network and the cluster detection. We present evidence that the detected clusters and core individuals reflect real life communities and influential developers.

Approximative methods are widely used in the resolution of complex computational problems, since they are able to present significant results regarding the quality of the solution in a satisfactory time in general. This paper addresses the problem of automatic grouping, admittedly NP-difficult. In the context of this problem, non-exact methods, whose complexity is polynomial, are desirable. To solve the problem, the work presents a metaheuristic based on collective intelligence, inspired by the behavior of the ants. The silhouette index was used to measure the quality of the generated clusters. The results obtained by the proposed algorithm were compared with the literature and indicated that the algorithm is able to find clusters that represent well the original distribution of the data.

This paper presents a solution based on analyses of complex networks in order to address context alignment and context loss between researchers working in projects and possibly in scientific experiments. Network edges with weights indicate the level of alignment between the researchers' contexts. The analyses showed that the context mismatch between members of a project might reveal the reasons why researchers quit their collaborative activities. It was also possible to identify the links that could support the decision of inserting a particular researcher into a project.

In the information age, people are getting more and more connected. In this context, collaborative activities are accomplished in different domains, and their relations allow researchers to model them as complex networks in order to obtain insights and competitive advantage. Software Ecosystems are emerging as a new way to contribute to developing and maintaining software through an active community of engaged users. Recognizing how users collaborate is a complex task that can help understand the network. This paper aims at analyzing a Software Ecosystem through its social layer. To this end, different complex networks were modeled using data extracted from the Eclipse ecosystem version control system. The networks represent interactions among users and projects. The network analyses conducted indicate the viability of using this approach to recognize important contributors in the ecosystem.

Social iterations in the scientific environment can be analyzed to enhance collaboration between researchers. Scientific social networks are complex networks that represent researchers' iterations through academic tasks. Analyzing the structure of those networks researchers can establish new relationships and to understand the potential of collaboration of their relationships. In this paper we use topological aspects from a Brazilian scientific social network to identify the key researchers to the collaboration flow. The relationships between researchers are used to provide insights about researchers' influence in the network.

Analyzing social iterations in a scientific environment will assist researchers in expanding their collaborative networks. Scientific social networks represent the researchers' social iterations in an academic environment. The analysis of these networks requires a detailed study of their structure and it is important the use of visual resources in order to a better understanding of how the social iterations occur. In this paper we will use centrality metrics and a clustering algorithm to analyze the structure of a Brazilian scientific social network. A scientific social network visualization tool will be used to allow a visual analysis of the collaboration between researchers from different educational institutions.

With the advance of mobile applications market, there is an increasing concern about the challenges when developing products that meet the many types of users and to harmonize each product with the various usage environments, thus, providing a good user experience. This situation leads to the need to develop a framework that makes applications become aware of the context and provides a self-learning of the user navigation. This article presents a theoretical framework and the TURAP operation process, a framework for Android applications that helps to tackle these challenges.

In recent years the scientific research has undergone substantial changes. In particular, there is a greater collaboration between research groups, which leads to an increase in the use of information processing techniques, and, therefore, the need to share results and observations among participants of a research. This work has as main goal to propose an architecture to support distributed processing of scientific experiments, as an implementation of so-called collaborative laboratories.

Social networks are dynamic social structures consisting of individuals or organizations, usually represented by nodes tied by one or more types of relationships. Analyzing these structures allows us to detect several inter and intra connections between people, inside and outside their organizations. In this context, we construct a multi-relational scientific social network where researchers may have four different types of relationships with each other. Using clustering techniques with max flow measure, we identify the social structure and research communities in a way that allows us to evaluate the knowledge flow in the Brazilian scientific scenario of the Computing Sciences.

A social network is a social structure consisting of individuals or organizations usually represented by nodes tied by one or more types of relationships. The resulting structures are complex and analyzing them enables us to detect several inter and intra connection problems. This work focuses on using data mining techniques to identify intra and inter organization groups of people with similar profiles that could have relationships among them. The clusters identified allow us to identify the collaboration in the Brazilian scientific scenario of Computing Science, assessing how researchers in the best universities and research centres collaborate and relate to each other. This kind of approach can help in the identification and improvement of collaboration on innovative and multidisciplinary teams, especially on manufacturing and design scenarios.