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.

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.

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.

[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.

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.

Summary Scientific workflows are used for dealing with complex problems in different e‐science domains. These workflows are modeled and executed using Scientific Workflow Management Systems (SWfMSs). Generally, SWfMSs provide their own Workflow Specification Language, and this is a challenge considering the possibility of interchanging workflow specifications between different SWfMSs. In this paper, we propose the use of workflow patterns combined with software architecture concepts to capture the key semantics expressed in scientific workflows specified in different Workflow Specification Languages. These concepts are materialized in a novel specification interchange language (and associated tooling) called Workflow Interchange in Science with Patterns (WISP), which is the main contribution of our work. We have evaluated WISP based on real world specifications; in this paper, we present the results of such evaluation (including an assessment of the quality of the research material available), which has eventually led us to reformulate our initial proposal with more stringent assumptions on the input and output specifications. Copyright © 2016 John Wiley & Sons, Ltd.

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.

Scientific workflows are used for dealing with complex problems in different e-science domains. These workflows are modeled and executed using Scientific Workflow Management Systems (SWfMSs). Generally, SWfMSs provide their own Workflow Specification Language (WfSL), and this is a challenge considering the possibility of interchanging workflow specifications between different SWfMSs. Nevertheless, the reuse of workflows gains growing importance as it helps with fostering the collaboration and cross-fertilization across different research groups. This paper presents a research proposal, including its mishaps and assimilations, on the use of workflow patterns combined with software architecture concepts to capture the key semantics expressed in scientific workflows specified in different WfSLs and to allow the interchanging of these specifications between different SWfMSs. This paper also shows how our findings based on real world specifications led us to reformulate our initial proposal and discuss the new results.

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.

Software patterns are computing artifacts used to document knowledge that may be reused during software development process.There are several types of patterns, such as analysis, design, and architectural, among others.Design patterns are the most well known by designers, but many of them are described in books and scientific papers, a recurring way of documenting patterns that limits their reuse potential.Aiming to not only minimize this limitation but also provide ways of recovering contextualized knowledge in these patterns, the present paper presents the architecture APRImora, an extension based on Semantic Web of the Analysis Patterns Reuse Infrastructure (APRI).In this architecture, the patterns are documented by metadata defined as application profiles of the Dublin Core standard and stored in the RDF format, allowing them to be discovered by search engines.The APRImora architecture helps designers discover and reuse software patterns based on semantic relations, which favors their dissemination and reuse.

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.