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.

Global Software Development (GSD) team members face barriers due to different location, time or culture that can directly influence the coordination of these distributed teams. As social problem solving approach to software development, Crowdsourcing is also facing coordination issues and these challenges must be overcome. For better understanding the issues, barriers and proposed solutions we conducted a systematic mapping. We identified 532 papers regarding coordination in crowdsourced software development, aiming to classify the most frequent features concerning crowdsourcing pillars and highlight the best practices to crowdsourcing coordination.

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.

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.

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.

Sentiment Analysis is a research area that has been also focused on software developers in the latest years. However, there is a need to map the way in which this area is structured. The goal of this article is to answer research questions through a systematic mapping in order to be aware of the existing works in this area. As a result, a growth curve related to the number of publications has been perceived as well as the association between the emotional condition of a developer and his/her performance in software projects.

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.

Summary Providing historical information to deal with knowledge loss about a scientific experiment has been the focus of some several researches. However, computational support for large‐scale scientific experiments is still incipient and is considered one of e‐science's greatest challenges. In this vein, providing provenance information to a scientist is part of these challenges. Provenance information helps to assure the reliability and reproducibility of experiments. Therefore, this work has as its main objective to present a new ontology—Open Provenance Model Ontology‐e (OPMO‐e)—which is part of an architecture, named SciProvMiner. Open Provenance Model Ontology‐e is a new ontology that encompasses both prospective and retrospective provenance. SciProvMiner captures the provenance and implements all inference and completeness rules defined by Open Provenance Model to provide provenance information beyond those already established. We hypothesize that the capture and management of provenance data will provide scientists with implicit strategical information about the experiment through OPMO‐e. Case studies were carried out to evaluate OPMO‐e use considering SciProvMiner. The obtained results revealed that the use of the proposed ontology and SciProvMiner can enhance scientist's knowledge about an experiment.

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.

Companies have increasingly distributed their teams to increase productivity and quality, and to reduce their costs in software development processes. This scenario has brought new challenges to effective collaboration and to enhance communication across peers. Pragmatic interoperability has been considered as one of the key requirements to tackle these challenges. However, there is lack of research investigating how to support development of pragmatic interoperability across globally dispersed groups. This paper proposes a model to support pragmatic interoperability in software projects to foster collaboration in distributed environments. The proposed model was implemented and evaluated in a collaborative architecture which supports collaborative scientific experiments development.

In scientific scenario, there is an increasing collaboration among research groups, which leads to the growth in the use of information processing techniques, and the creation of new experiments as well as the sharing of their results. In this article, we present an architecture named e-ScienceNet, that creates an access point to distributed scientific applications, where scientists can work with heterogeneous data, form groups based on their interests, and create new experiments based on service compositions. Therefore, e-ScienceNet, a semantic peer-to-peer network system, aims to provide support to scientist in the e-Science context, enabling the use and sharing of scientific resources.

Collaborative Systems can be constructed through Software Product Line (SPL) with the support of the Model-Driven Development (MDD) approach. However, models generated as SPL artifacts usually present only feature classification. Such models neither express the purpose of each SPL artifact of the systems composition nor the way they were associated with the collaboration elements. Therefore, to support the analysis and design of collaborative systems it is relevant to classify artifacts with the support of a collaborative model. This paper describes a way a collaboration model can be used to construct a UML profile. This profile is applied to MDD models that represent collaboration artifacts, enhancing the classification, location and reuse of artifacts in order to support the development of Collaborative Systems.

Middleware infrastructure has been widely adopted to support the development and execution of collaborative systems. With these systems many challenges arise, those related to interoperability gained remarkable importance. This work aims to increase the semantic interoperability support in an infrastructure named Web-based Groupware Service-Oriented Architecture (WGWSOA) that uses middleware services to support the development and execution of collaborative systems. Services were built on this infrastructure to extend the interoperability semantic support. Evidences pointing to the validity of the proposed solution, to support the development and execution of collaborative systems, were produced through an experimental study.

The Web-based Groupware Service-Oriented Architecture (WGWSOA) is an infrastructure that uses middleware services to support collaborative system development. In addition, it aims to support the issue of semantic interoperability conflict resolution for middleware services in the construction of collaborative systems in heterogeneous environments. Interoperability is an essential requirement for service implementation that supports developments in these environments and as a result, this is an important factor for the interaction of user groups in collaborative systems. In this context, the goal of this paper is to describe a solution to widen semantic interoperability support in an infrastructure whose goal is to support collaborative system development and execution.

With the increasing communication demand among distributed collaborative applications, software developers are facing new challenges related to semantic interoperability issues. This article aims to present a solution based on a mediation service and ontology in order to support interoperability across specific middleware services. These services are part of a middleware infrastructure which was designed to support groupware development. To illustrate our proposal, we discuss some problems usually found when distributed groupware applications interoperation is required. Furthermore, we focus our evaluation on a mediation approach to support the interoperability in a middleware infrastructure.

Fulfilling composability and reusability requirements is one of the main challenges of service-oriented software engineering. This paper describes research which applies a MDA based-process to support the development of specific middleware services in WGWSOA infrastrucuture. This process aims to comply with EDOC and RM-ODP profiles in order to guide specific services development of an architecture based on middleware services for collaborative applications. We also evaluate if the process deployment could enhance composability and reusability requirements.

This paper aims to present an awareness service, named Aw2SOA, which was developed in order to support distributed groupware design in WGWSOA environment. It is a middleware specific service which was implemented according to SOA principles and aspect-oriented concepts. In groupware literature, service-oriented approach is suggested aiming to solve reuse and evolution requirements. However, awareness support is fulfilled by similar functionalities implemented in different modules. As a result, activities related to the maintenance and evolution of this matter cannot be considered as easy tasks. WGWSOA (Web- based Groupware Service-Oriented Architecture) is based on middleware services for collaborative applications.

Collaborative Systems are applications that demand for the distribution support of their resources. Usually, middle-wares have been deployed as a development platform for such applications. In spite of manipulating objects (documents, meetings, agenda, discussion list, etc.) with same semantics, it is not possible for an application to access both objects and data of another distinguishable application, even though they have been built by the use of the same middleware platform. The complexity of this scenario becomes larger when we idealize a project that demand for the interoperability of groupware applications developed by the use of distinct middlewares. The goal of this paper is to present an architecture based on middleware services -named WGWSOA (Web-based Groupware Service Oriented Architecture) -for Collaborative Systems. From this architecture, we hope to offer specific services in order to leverage the interoperability concerns of such systems.

Research has been done in order to propose solutions that enhance interactions among participants and improve activity coordination in the meeting cycle. Some of these are related to pre-meeting phase conduction. We argue that collaboration could be enhanced if we supplied relevant context information through awareness mechanisms. This paper aims at evaluating context elements in an asynchronous web-based pre-meeting application. As a result, a context framework is discussed in order to support awareness design in the meeting support applications domain.

Many awareness mechanisms in groupware systems have been searching to reduce the amount of information found in the workspace where activities develop. Several approaches and proposals found in literature are based on filter usage for best identifying and presenting information according to the users choices or previous ways of working. The goal of the paper is to present a strategy to both guide the selection of information from the group memory and support coordination actions in groupware applications. This proposal is based on filters and awareness profiles established by the participants in groupware activities.