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The months an R-help user has been active according to whether they only ask questions or only answer questions (and potentially ask questions, too). People who answer questions tend to stay around much longer than those who only ask questions
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One of the effects of social media’s prevalence in software development is the many flourishing communities of practice where users share a common interest. These large communities use many different communication channels, but little is known about how they create, share, and curate knowledge using such channels. In this paper, we report a mixed m...
Citations
... As the same person may have multiple email addresses, disambiguation techniques are often required to uniquely identify a given team member [184]. They have been the subject of multiple empirical studies (e.g., [74,189]). Some of these studies have tried to identify personality traits or emotions expressed through e-mails [98,168,147]. ...
... Such Q&A platforms can be considered as a software ecosystem where the "components" are questions and their answers (including all the metadata that comes with them), and the contributor community consists of developers that are asking questions, and experts that provide answers to these questions. The StackOverflow ecosystem has been studied for various purposes and in various ways [126,8,15,125,189,13,110,5,193,176,177]. The open dataset SOTorrent has been made available on top of a datadump with all posts from 2018 till 2020 [11,12,10]. ...
This chapter defines and presents different kinds of software ecosystems. The focus is on the development, tooling and analytics aspects of software ecosystems, i.e., communities of software developers and the interconnected software components (e.g., projects, libraries, packages, repositories, plug-ins, apps) they are developing and maintaining. The technical and social dependencies between these developers and software components form a socio-technical dependency network, and the dynamics of this network change over time. We classify and provide several examples of such ecosystems. The chapter also introduces and clarifies the relevant terms needed to understand and analyse these ecosystems, as well as the techniques and research methods that can be used to analyse different aspects of these ecosystems.
... Some work extended the focus to individual and group activities which are guided by values. These activities include collaboration and coordination [52,58,69], licensing [16,35], governance & decision-making [34,52,70], and knowledge sharing [51,102]. From a structural perspective, researchers deconstructed how individuals form communities. ...
Encompassing a diverse population of developers, non-technical users, organizations, and many other stakeholders, open source software (OSS) development has expanded to broader social movements from the initial product development aims. Ideology, as a coherent system of ideas, offers value commitments and normative implications for any social movement, so does OSS ideology for the open source movement. However, the literature on open source ideology is often fragile, or lacking in empirical evidence. In this paper, we sought to develop a comprehensive empirical theory of ideologies in open source software movement. Following a grounded theory procedure, we collected and analyzed data from 22 semi-structured interviews and 41 video recordings of Open Source Initiative (OSI) board members' public speeches. An empirical theory of OSS ideology emerged in our analysis, with six key categories: membership, norms/values, goals, activities, resources, and positions/group relations; each consists of a number of themes and subthemes. We discussed a subset of carefully selected themes and subthemes in detail based on their theoretical significance. With this ideological lens, we examined the implications and insights into open source development, and shed light on the research into open source as a social-cultural construction in the future.
... As the same person may have multiple email addresses, disambiguation techniques are often required to uniquely identify a given team member [184]. They have been the subject of multiple empirical studies (e.g., [74,189]). Some of these studies have tried to identify personality traits or emotions expressed through e-mails [98,168,147]. ...
... Such Q&A platforms can be considered as a software ecosystem where the "components" are questions and their answers (including all the metadata that comes with them), and the contributor community consists of developers that are asking questions, and experts that provide answers to these questions. The StackOverflow ecosystem has been studied for various purposes and in various ways [126,8,15,125,189,13,110,5,193,176,177]. The open dataset SOTorrent has been made available on top of a datadump with all posts from 2018 till 2020 [11,12,10]. ...
This chapter defines and presents the kinds of software ecosystems that are targeted in this book. The focus is on the development, tooling and analytics aspects of "software ecosystems", i.e., communities of software developers and the interconnected software components (e.g., projects, libraries, packages, repositories, plug-ins, apps) they are developing and maintaining. The technical and social dependencies between these developers and software components form a socio-technical dependency network, and the dynamics of this network change over time. We classify and provide several examples of such ecosystems, many of which will be explored in further detail in the subsequent chapters of the book. The chapter also introduces and clarifies the relevant terms needed to understand and analyse these ecosystems, as well as the techniques and research methods that can be used to analyse different aspects of these ecosystems.
... Sin embargo, todavía existen limitaciones para estos o dificultades en términos de uso (e.g., en el caso del uso de marcadores el desarrollador puede olvidar las etiquetas o el propósito del mismo) [48]. ...
To support their knowledge needs, finding relevant expertise is a critical need in software organizations. Developers use it and accumulate expertise from the technologies they use and the problems solved within projects. Currently, members of the organizations have yet to benefit from this expertise. Learning how to model it might enable access to the best practices and problem-solved information. This work presents a model that implements the knowledge condensation concept to capture expertise during the software development process. The aim is to classify, retrieve, and share valuable knowledge among stakeholders in an unsuitable form for its recovery. The model consists of three modules: formal grammar, semantic knowledge, and expertise tools. The formal grammar module presents an approach to formalize how developers store and share their knowledge. The semantic knowledge module presents an architectural knowledge model. Finally, we presented two prototypes in the expertise tools module that use the semantic knowledge module elements.
... SO has shown to be the most prominent community Q&A site for knowledge sharing and learning in software development, and SO leverages the knowledge and skills of its users, such as developers, to share their thoughts and experience by asking various types of technical questions related to development and providing answers to these questions. Also, SO users can learn novel techniques and tools from SO [2]. SO is predominately being used to solve coding problems [3], and these problems are often not relevant or less interesting to architects because they focus on lower-level implementation details [3]. ...
... Moreover, there has been no comprehensive research on exploring architectural knowledge communicated by SO users in terms of their types, design contexts, characteristics, and usefulness, which is the focus of this study. Analyzing and understanding how SO users deal with architecture design concerns in online developer communities, such as SO, brings three benefits: (1) it provides key insights about the types of design problems SO users face during their architecture design and the types of architecture solutions discussed as well as their usefulness, (2) it can help to know the design contexts in which architecture problems are raised, and (3) it can help to know the characteristics of architecture problems and solutions discussed. These benefits provide an opportunity to develop new techniques and tools that can help SO users search and (re)use architectural knowledge shared in online developer communities. ...
... The main results and findings of this study are that: (1) SO users ask a broad spectrum of architecture related questions ranging from architecture tool to architecture configuration, architecture implementation to architecture deployment. (2) The useful architecture solutions are classified into seven categories as a taxonomy (see Figure 4), such as solution for architecture configuration, solution for architecture implementation, architecture tactic, and architecture pattern. One observation is that the identified categories of these posts (questions and answers) cover almost all the architecting activities that span from the initial stages (i.e., architectural analysis and synthesis [18]) of architectural creation as well as the later stages (i.e., architectural implementation and maintenance & evolution [19]) in a system lifecycle. ...
Context: Stack Overflow (SO) has won the intention from software engineers (e.g., architects) to learn, practice, and utilize development knowledge, such as Architectural Knowledge (AK). But little is known about AK communicated in SO, which is a type of high-level but important knowledge in development. Objective: This study aims to investigate the AK in SO posts in terms of their categories and characteristics as well as their usefulness from the point of view of SO users. Method: We conducted an exploratory study by qualitatively analyzing a statistically representative sample of 968 Architecture Related Posts (ARPs) from SO. Results: The main findings are: (1) architecture related questions can be classified into 9 core categories , in which "architecture configuration" is the most common category, followed by the "architecture decision" category, and (2) architecture related questions that provide clear descriptions together with architectural diagrams increase their likelihood of getting more than one answer, while poorly structured architecture questions tend to only get one answer. Conclusions: Our findings suggest that future research can focus on enabling automated approaches and tools that could facilitate the search and (re)use of AK in SO. SO users can refer to our proposed guidelines to compose architecture related questions with the likelihood of getting more responses in SO.
... According to the community shared experiences, this issue can be resolved by taking a hot shower or by putting a warm compress on the blocked area of the breast. To decide which of the solutions is effective, we applied our proposed confidence degree measure in (2). According to Table 3, "hot shower" is more likely to be a good solution for clogged duct issue with a confidence degree of 22.7% compared to "warm compress" where its confidence degree is 4.5%. ...
Online communities are a real medium for human experiences sharing. They contain rich knowledge of lived situations and experiences that can be used to support decision-making process and problem-solving. This work presents an approach for extracting, representing, and evaluating components of problem-solving knowledge shared in online communities. Few studies have tackled the issue of knowledge extraction and its usefulness evaluation in online communities. In this study, we propose a new approach to detect and evaluate best solutions to problems discussed by members of online communities. Our approach is based on knowledge graph technology and graphs theory enabling the representation of knowledge shared by the community and facilitating its reuse. Our process of problem-solving knowledge extraction in online communities (PSKEOC) consists of three phases: problems and solutions detection and classification, knowledge graph constitution and finally best solutions evaluation. The experimental results are compared to the World Health Organization (WHO) model chapter about Infant and young child feeding and show that our approach succeed to extract and reveal important problem-solving knowledge contained in online community's conversations. Our proposed approach leads to the construction of an experiential knowledge graph as a representation of the constructed knowledge base in the community studied in this paper.
... This shows that knowledge exchange and technology use have a dynamic effect on individuals' willingness to participate in the online process. The integration of these elements also categorized knowledge sharing in two different forms, i.e., scattered and aggregated (Zagalsky et al., 2018). According to Tausczik, several influential factors of the sharing behavior were summarized, such as Technology, Motivation and Individual differences, as well as Group dynamics, which all involved information exchange and technological use (Tausczik & Huang, 2020). ...
This study aims to explore the significant variables affecting online knowledge-sharing and the hierarchical structure, from the perspective of online learners. To comprehensively discuss the relationship between these variables, binary logit regression and interpretative structural model (ISM) was used. Based on literature analysis, the data of 29 candidates were obtained, and 670 valid data was acquired through an electronic questionnaire. A total of 13 significant variables were also obtained using the Logit model of SPSS 22, with an 8-layer ISM program established by MATLAB 2017A software. The results showed that six of the 13 variables had positive effects on online knowledge-sharing behavior, with the remaining seven having a negative impact. The ISM model also proved that trust and delete/block, reward, and the remaining elements were shallow, deep, and intermediate variables, respectively. Combining the Logit and ISM advantages, these results strengthened the reports on online knowledge-sharing behavior, subsequently obtaining five suggestions for its development. This study is expected to help teachers and online course developers design better digital programs, as well as ensure the accurate decision-making of students in knowledge sharing activities.
... Поскольку сообщества вузов поддерживают массовую коммуникацию и социальное взаимодействие как один из каналов общественного мнения [8], это позволяет проводить исследования, где подобные сообщества сравниваются с каналами связи. Например, исследования списков рассылки [9], сайтов вопросов и ответов [10], микроблогов [11], новостных агрегаторов [12]. Так, в работе [13] проанализированы темы обсуждения с использованием метода моделирования мнений, а М. Сквайр [14] изучал переход от стихийных форумов к модерируемым обсуждениям. ...
The article presents the results of the analysis of users’ sentiment in social networks, performed using big data tools. The research was aimed at developing the methodology, which enables to analyze the content of social networks, assess students’ attitude to the transition to online learning in conditions of COVID-19 pandemic, identify dynamics and main trends in student satisfaction with the quality of educational process. We explored about 2 million posts and comments posted in university social networks (more than 1000 university public pages) for the period from Sept 2020 to July 2021. Special attention was paid to the problems of communication between students and teachers, strategies to solve them, an emotional reaction. PolyAnalyst software was applied for data precleaning. It has been found that the main problem affecting the quality of education is a change in the mechanisms of interaction between students and teachers. Based on student publications in social networks, we have identified the strategies for adapting students to online learning. We came to a conclusion that teachers’ support of students is crucial in preventing and solving social and academic problems in conditions of online learning. One of the ways to improve interaction between students and teachers, raise students’ involvement is using discussion forums, chats in messengers for academic purposes, and providing teachers’ methodical support.
... The software engineering community considers Stack Overflow as a learning site and a learning community for software developers and practitioners [1], [2]. Comments in Stack Overflow (SO) are temporary "Post-It" notes relevant to a particular question or an answer which has already been posted [3]. They clarify and enrich the content conveyed through questions and answers. ...
Stack Overflow is a public platform for developers to share their knowledge on programming with an engaged community. Crowdsourced programming knowledge is not only generated through questions and answers but also through comments which are commonly known as developer discussions. Despite the availability of standard commenting guidelines on Stack Overflow, some users tend to post comments not adhering to those guidelines. This practice affects the quality of the developer discussion, thus adversely affecting the knowledge-sharing process. Literature reveals that analyzing the comments could facilitate the process of learning and knowledge sharing. Therefore, this study intends to extract and classify useful comments into three categories: request clarification, constructive criticism, and relevant information. In this study, the classification of useful comments was performed using the Support Vector Machine (SVM) algorithm with five different kernels. Feature engineering was conducted to identify the possibility of concatenating ten external features with textual features. During the feature evaluation, it was identified that only TF-IDF and N-grams scores help classify useful comments. The evaluation results confirm Radial Basis Function (RBF) kernel of the SVM classification algorithm performs best in classifying useful comments in Stack Overflow regardless of the usage of the optimal combinations of hyperparameters.
... Squire [53] studied the underlying reasons and the effectiveness of 20 open source projects which moved their user support from mailing lists or forums to Stack Overflow. Zagalsky et al. [54] made a comparative study between R community in Stack Overflow and R community in mailing lists regarding types of questions being asked and how knowledge is constructed. Although those related work focused on multiple communities of the same topic concurrently, they focused on communities with totally different structures, such as mailing lists and Stack Overflow. ...
Cybersecurity affects us all in our daily lives. New knowledge on best practices, new vulnerabilities, and timely fixes for cybersecurity issues is growing super-linearly, and is spread across numerous, heterogeneous sources. Because of that, community contribution-based, question and answer sites have become clearinghouses for cybersecurity-related inquiries, as they have for many other topics. Historically, Stack Overflow has been the most popular platform for different kinds of technical questions, including for cybersecurity. That has been changing, however, with the advent of Security Stack Exchange, a site specifically designed for cybersecurity-related questions and answers. More recently, some cybersecurity-related subreddits of Reddit, have become hubs for cybersecurity-related questions and discussions. The availability of multiple overlapping communities has created a complex terrain to navigate for someone looking for an answer to a cybersecurity question. In this paper, we investigate how and why people choose among three prominent, overlapping, question and answer communities, for their cybersecurity knowledge needs. We aggregated data of several consecutive years of cybersecurity-related questions from Stack Overflow, Security Stack Exchange, and Reddit, and performed statistical, linguistic, and longitudinal analysis. To triangulate the results, we also conducted user surveys. We found that the user behavior across those three communities is different, in most cases. Likewise, cybersecurity-related questions asked on the three sites are different, more technical on Security Stack Exchange and Stack Overflow, and more subjective and personal on Reddit. Moreover, there appears to have been a differentiation of the communities along the same lines, accompanied by overall popularity trends suggestive of Stack Overflow’s decline and Security Stack Exchange’s rise within the cybersecurity community. Reddit is addressing the more subjective, discussion type needs of the lay community, and is growing rapidly.
